Quantized Synchronization Control of Networked Nonlinear Systems: Dynamic Quantizer Design With Event-Triggered Mechanism

Dissipative control for nonlinear singularly perturbed systems with dynamic quantization and actuator failure

The paper discusses the dissipative control problem of nonlinear singularly perturbed systems (SPSs) with dynamic quantization and actuator failure. The nonlinear singularly perturbed plant is described by Takagi-Sugeno (T-S) fuzzy model. The dynamic quantizer is considered to realize the effective utilization of the limited network channel bandwidth resources, and the possible transmission failure between the controller and the actuator is also considered. Based on linear matrix inequalities, sufficient design conditions for the ϵ-independent state feedback controller are given such that the constructed quantized closed-loop system is asymptotically stable and satisfies a predefined dissipative performance. Furthermore, a short search algorithm is given to search the maximum stability bound ϵ̄. Finally, two examples are provided in order to prove the feasibility and effectiveness of the proposed method.

A Simple Federated Learning-Based Scheme for Security Enhancement Over Internet of Medical Things

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has become a current research hotspot. As a new architecture, FL can well protect the data privacy of IoMT devices, but the security of neural network model transmission can not be guaranteed. On the other hand, the sizes of current popular neural network models are usually relatively extensive, and how to deploy them on the IoMT devices has become a challenge. One promising approach to these problems is to reduce the network scale by quantizing the parameters of the neural networks, which can greatly improve the security of data transmission and reduce the transmission cost. In the previous literature, the fixed-point quantizer with stochastic rounding has been shown to have better performance than other quantization methods. However, how to design such quantizer to achieve the minimum square quantization error is still unknown. In addition, how to apply this quantizer in the FL framework also needs investigation. To address these questions, in this paper, we propose FedMSQE - Federated Learning with Minimum Square Quantization Error, that achieves the smallest quantization error for each individual client in the FL setting. Through numerical experiments in both single-node and FL scenarios, we prove that our proposed algorithm can achieve higher accuracy and lower quantization error than other quantization methods.