Client Selection in Fault-Tolerant Federated Reinforcement Learning for IoT Networks
Semih Cal, Xiang Sun, Jingjing Yao · 2024
In wireless Internet of Things (IoT) networks, Federated Reinforcement Learning (FRL) has emerged as a decentralized strategy for data-driven decision-making, enabling devices to learn directly from real-time environmental interactions, sidestepping the need for labeled data. This method promises enhanced data privacy and finds practical applications in autonomous driving, smart grids, and industrial automation. However, the integrity of FRL can be compromised by malicious clients injecting false data, underlining the need for a fault-tolerant mechanism to sustain the robustness and accuracy of the learning phase. Moreover, the inherent client heterogeneity within IoT networks propels the demand for judicious client selection, optimizing computational and communication resources. This paper investigates client selection problem within a fault-tolerant FRL framework for wireless IoT networks. Our objective is to explore the tradeoff between maximizing client participation and minimizing energy consumption of IoT devices. We formulate our problem as a mixed-integer linear programming (MILP) model and design an efficient algorithm with low computational complexity to address it. Extensive simulations are conducted to demonstrate the superiority of our proposed algorithm.