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Hallaj S, Chuter BG, Lieu AC, Singh P, Kalpathy-Cramer J, Xu BY, Christopher M, Zangwill LM, Weinreb RN, Baxter SL. Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives. Ophthalmol Glaucoma 2024:S2589-4196(24)00143-1. [PMID: 39214457 DOI: 10.1016/j.ogla.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
Current approaches to developing artificial intelligence (AI) models for widespread glaucoma screening have encountered several obstacles. First, glaucoma is a complex condition with a wide range of morphological and clinical presentations. There exists no consensus definition of glaucoma or glaucomatous optic neuropathy. Further, training effective deep learning algorithms poses numerous challenges, including susceptibility to overfitting and lack of generalizability on external data. Therefore, training data should ideally be sourced from large, well-curated, multi-client cohorts to ensure diversity in patient populations, disease presentations, and imaging protocols. However, the construction of centralized repositories for multimodal data faces hurdles such as concerns regarding data sharing, re-identification, storage, regulations, patient privacy, and intellectual property. Federated learning (FL) has emerged as a proposed solution to address some of these concerns by enabling data to remain locally hosted while facilitating distributed model training. This article aims to provide a comprehensive review of the existing literature on FL in the context of its applications for AI tasks related to glaucoma.
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Affiliation(s)
- Shahin Hallaj
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Benton G Chuter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Alexander C Lieu
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Praveer Singh
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Benjamin Y Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA.
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Mehari M, Sibih Y, Dada A, Chang SM, Wen PY, Molinaro AM, Chukwueke UN, Budhu JA, Jackson S, McFaline-Figueroa JR, Porter A, Hervey-Jumper SL. Enhancing neuro-oncology care through equity-driven applications of artificial intelligence. Neuro Oncol 2024:noae127. [PMID: 39159285 DOI: 10.1093/neuonc/noae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024] Open
Abstract
The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.
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Affiliation(s)
- Mulki Mehari
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Youssef Sibih
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Abraham Dada
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Susan M Chang
- Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Annette M Molinaro
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Ugonma N Chukwueke
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua A Budhu
- Department of Neurology, Memorial Sloan Kettering Cancer Center, Department of Neurology, Weill Cornell Medicine, Joan & Sanford I. Weill Medical College of Cornell University, New York, New York, USA
| | - Sadhana Jackson
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Alyx Porter
- Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Shawn L Hervey-Jumper
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
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3
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Khatibi SMH, Ali J. Harnessing the power of machine learning for crop improvement and sustainable production. FRONTIERS IN PLANT SCIENCE 2024; 15:1417912. [PMID: 39188546 PMCID: PMC11346375 DOI: 10.3389/fpls.2024.1417912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/15/2024] [Indexed: 08/28/2024]
Abstract
Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.
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Affiliation(s)
| | - Jauhar Ali
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Laguna, Philippines
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4
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Liu G, Kang M, Zhu Y, Zheng Q, Zhu M, Li N. TransNeural: An Enhanced-Transformer-Based Performance Pre-Validation Model for Split Learning Tasks. SENSORS (BASEL, SWITZERLAND) 2024; 24:5148. [PMID: 39204844 PMCID: PMC11359476 DOI: 10.3390/s24165148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/25/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
While digital twin networks (DTNs) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for split learning (SL) tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreported resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 9.3% and convergence estimation accuracy by 22.4% compared to traditional equation-based methods.
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Affiliation(s)
- Guangyi Liu
- China Mobile Research Institute, Beijing 100053, China; (G.L.); (Q.Z.); (N.L.)
| | - Mancong Kang
- School of Communications and Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yanhong Zhu
- China Mobile Research Institute, Beijing 100053, China; (G.L.); (Q.Z.); (N.L.)
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100091, China
| | - Qingbi Zheng
- China Mobile Research Institute, Beijing 100053, China; (G.L.); (Q.Z.); (N.L.)
- ZGC Institute of Ubiquitous-X Innovation and Application, Beijing 100191, China
| | - Maosheng Zhu
- China Mobile (Suzhou) Software Technology Co., Ltd., Suzhou 215163, China;
| | - Na Li
- China Mobile Research Institute, Beijing 100053, China; (G.L.); (Q.Z.); (N.L.)
- ZGC Institute of Ubiquitous-X Innovation and Application, Beijing 100191, China
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Messinis SC, Protonotarios NE, Doulamis N. Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:5142. [PMID: 39204839 PMCID: PMC11360477 DOI: 10.3390/s24165142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 07/30/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.
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Affiliation(s)
- Sotirios C. Messinis
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece;
| | | | - Nikolaos Doulamis
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece;
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6
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Bergstrom EN, Alexandrov LB. Enhanced precision in immunotherapy. NATURE CANCER 2024; 5:1136-1138. [PMID: 39134713 DOI: 10.1038/s43018-024-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Affiliation(s)
- Erik N Bergstrom
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Ludmil B Alexandrov
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Sanford Stem Cell Institute, University of California San Diego, La Jolla, CA, USA.
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7
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Winder AJ, Stanley EA, Fiehler J, Forkert ND. Challenges and Potential of Artificial Intelligence in Neuroradiology. Clin Neuroradiol 2024; 34:293-305. [PMID: 38285239 DOI: 10.1007/s00062-024-01382-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/03/2024] [Indexed: 01/30/2024]
Abstract
PURPOSE Artificial intelligence (AI) has emerged as a transformative force in medical research and is garnering increased attention in the public consciousness. This represents a critical time period in which medical researchers, healthcare providers, insurers, regulatory agencies, and patients are all developing and shaping their beliefs and policies regarding the use of AI in the healthcare sector. The successful deployment of AI will require support from all these groups. This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research. METHODS A review of relevant guidelines and literature describing how scientific reporting plays a central role at key stages in the life cycle of an AI software product was conducted. To contextualize this principle within a specific medical domain, we discuss the current state of predictive tissue outcome modeling in acute ischemic stroke and the unique challenges presented therein. RESULTS AND CONCLUSION Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers' reservations regarding AI's efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI. In all cases, establishing and adhering to appropriate reporting standards is an important responsibility that is shared by all of the parties involved in the life cycle of a prospective AI software product.
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Affiliation(s)
- Anthony J Winder
- Department of Radiology, University of Calgary, Calgary, Canada.
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
| | - Emma Am Stanley
- Department of Radiology, University of Calgary, Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada
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van Genderen ME, Cecconi M, Jung C. Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care. Intensive Care Med 2024; 50:974-977. [PMID: 38635044 PMCID: PMC11164808 DOI: 10.1007/s00134-024-07408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/23/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Michel E van Genderen
- Department of Adult Intensive Care, Erasmus MC, University Medical Center Rotterdam, (internal postadress-Room Ne-403), Doctor molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| | - Maurizio Cecconi
- Biomedical Sciences Department, Humanitas University, Milan, Italy
- Department of Anaesthesia and Intensive Care, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Christian Jung
- Medical Faculty, Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
- Medical Faculty and University Hospital of Düsseldorf, Cardiovascular Research Institute Düsseldorf (CARID), Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany
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Ahmed ST, Mahesh TR, Srividhya E, Vinoth Kumar V, Khan SB, Albuali A, Almusharraf A. Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework. BMC Med Imaging 2024; 24:105. [PMID: 38730390 DOI: 10.1186/s12880-024-01279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.
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Affiliation(s)
- Syed Thouheed Ahmed
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, 502285, India
| | - T R Mahesh
- Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bengaluru, 562112, India
| | - E Srividhya
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, 600119, India
| | - V Vinoth Kumar
- School of Computer Science Engineering & Information Systems(SCORE), Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India
| | - Surbhi Bhatia Khan
- School of Science Engineering and Environment, University of Salford, Manchester, UK.
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
| | - Abdullah Albuali
- College of Computer Sciences and Information Technology, King Faisal University, 31982, Hofuf, Saudi Arabia
| | - Ahlam Almusharraf
- Department of management, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
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Chen X, Feng Z, Zhang JA, Yang Z, Yuan X, He X, Zhang P. Integrated Communication, Sensing, and Computation Framework for 6G Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:2968. [PMID: 38793823 PMCID: PMC11125370 DOI: 10.3390/s24102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/20/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024]
Abstract
In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.
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Affiliation(s)
- Xu Chen
- China Academy of Information and Telecommunications Technology (CAICT), Beijing 100191, China;
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Zhiyong Feng
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - J. Andrew Zhang
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia; (J.A.Z.); (X.Y.)
| | - Zhaohui Yang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310007, China;
- Zhejiang Provincial Key Lab of Information Processing, Communication and Networking (IPCAN), Hangzhou 310007, China
| | - Xin Yuan
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia; (J.A.Z.); (X.Y.)
| | - Xinxin He
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Ping Zhang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
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Mengistu TM, Kim T, Lin JW. A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:968. [PMID: 38339685 PMCID: PMC10857305 DOI: 10.3390/s24030968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.
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Affiliation(s)
| | - Taewoon Kim
- Department of Information Convergence Engineering, Pusan National University, Busan 46241, Republic of Korea;
| | - Jenn-Wei Lin
- Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
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12
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Lee T, Wollstein G, Madu CT, Wronka A, Zheng L, Zambrano R, Schuman JS, Hu J. Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality. Transl Vis Sci Technol 2023; 12:2. [PMID: 38038606 PMCID: PMC10697175 DOI: 10.1167/tvst.12.12.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Purpose Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.
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Affiliation(s)
- TingFang Lee
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Departments of Population Health, NYU Langone Health, New York, NY, USA
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Center of Neural Science, NYU College of Arts and Sciences, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Chisom T Madu
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Andrew Wronka
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Lei Zheng
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Ronald Zambrano
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Joel S Schuman
- Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA
| | - Jiyuan Hu
- Departments of Population Health, NYU Langone Health, New York, NY, USA
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Li LS, Yang L, Zhuang L, Ye ZY, Zhao WG, Gong WP. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. Mil Med Res 2023; 10:58. [PMID: 38017571 PMCID: PMC10685516 DOI: 10.1186/s40779-023-00490-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/06/2023] [Indexed: 11/30/2023] Open
Abstract
Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.
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Affiliation(s)
- Lin-Sheng Li
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China
- Hebei North University, Zhangjiakou, 075000, Hebei, China
- Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China
| | - Ling Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Li Zhuang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhao-Yang Ye
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Wei-Guo Zhao
- Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China.
| | - Wen-Ping Gong
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, China.
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Zhang B, Lu G, Qiu P, Gui X, Shi Y. Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1550. [PMID: 37998241 PMCID: PMC10670442 DOI: 10.3390/e25111550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023]
Abstract
Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.
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Affiliation(s)
| | | | | | | | - Yang Shi
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (B.Z.); (G.L.); (P.Q.); (X.G.)
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15
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Khajehali N, Yan J, Chow YW, Fahmideh M. A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods. SENSORS (BASEL, SWITZERLAND) 2023; 23:7235. [PMID: 37631771 PMCID: PMC10459674 DOI: 10.3390/s23167235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/12/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of this SLR is to facilitate future research and development of CS methods in FL. Additionally, a detailed and in-depth overview of the CS process is provided, encompassing its abstract implementation and essential characteristics. This comprehensive presentation enables the application of CS in diverse domains. Furthermore, various CS methods are thoroughly categorized and explained based on their key characteristics and their ability to address specific challenges. This categorization offers valuable insights into the current state of the literature while also providing a roadmap for prospective investigations in this area of research.
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Affiliation(s)
- Naghmeh Khajehali
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Jun Yan
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Yang-Wai Chow
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia; (J.Y.); (Y.-W.C.)
| | - Mahdi Fahmideh
- School of Business, University of Southern Queensland (USQ), Brisbane, QLD 4350, Australia;
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