A Federated Learning Client Selection Method via Multi-Task Deep Reinforcement Learning
Le Hou, Laisen Nie, Xinyang Deng · 2024
Federated Learning (FL) is a privacy-preserving paradigm for training machine learning (ML) models, crucial for data privacy and security protection. It has garnered significant attention from both industry and academia. Typically, clients are selected randomly for training and model aggregation in FL scenarios. However, heterogeneity in data distribution and hardware among devices leads to problems such as slow model convergence, low accuracy, and high computational overhead. To address the issues of statistical heterogeneity and system heterogeneity in FL, this paper proposes an intelligent client selection framework via multitask deep reinforcement learning (DRL). Additionally, two reward functions are introduced to alleviate the heterogeneity problem by maximizing model performance and minimizing system latency. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of the proposed method.