Decentralized-Voting-Based Federated Learning Framework for Lightweight Node Selection in Edge Collaborative IoT

Federated learning (FL) is an emerging distributed machine learning paradigm that has privacy-preserving properties, but still poses privacy leakage risks in traditional centralized FL (CFL) caused by the frequent transmission of model parameters during training. Due to the resource differences among nodes, the training process is constrained by the slowest node, while frequent data transmissions result in significant communication overhead, leading to the reduced overall efficiency. What is more, resource-rich nodes cannot fully utilize their potentials, and large models cannot be deployed on resource-constrained end devices. Therefore, selecting participating nodes and their local training strategies efficiently is a key issue in FL. To address the aforementioned issues, this article proposes an edge-cooperative decentralized lightweight FL framework (Dec-LWFL), which introduces a multiagent reinforcement learning (MARL) method, and designs a scoring voting mechanism for selecting participating FL nodes, determining their local training strategies, and allocating the model aggregation tasks. During agent interactions, Gaussian noise is added to the state information to safeguard the privacy of edge nodes and users, and Rényi differential privacy (RDP) is utilized to quantify the effectiveness of the privacy protection mechanism. To adapt to the resource-constrained IoT environment, Huffman coding is employed during FL training phase to compress the transmitted models and reduce the communication overhead; then, knowledge distillation is selected during the deployment phase to achieve the goal of lightweight deployment. Experimental results demonstrate that Dec-LWFL can effectively balance the privacy and performance. The framework can significantly optimize the training latency and energy consumption, while satisfying the requirements for lightweight deployment.