基于深度强化学习的联邦学习客户端自适应选择策略OA

Federated learning(FL)is a distributed paradigm for addressing data isolation by enabling global model training without clients sharing raw data,thereby protecting user privacy.Due to the large number of clients and limited communication resources,only a subset of clients can participate in model aggregation.Nonetheless,FL systems still face challenges such as device heterogeneity and data hetero-geneity.Naive client selection strategies fail to adapt to environmental dynamics,resulting in slow model convergence and degraded performance.To address these,this paper proposes a novel client availability metric by considering time-varying client states,and formulates a multi-constrained client selection model as a loss minimization problem.Then,this problem is further modeled as a Markov decision pro-cess,and a deep reinforcement learning-based adaptive client selection algorithm(ASC-DRL)is de-signed.ASC-DRL comprehensively optimizes communication latency,resource consumption,and client availability through continuous agent-environment interactions,maximizing a reward function,to obtain the optimal client selection scheme.Experiment results demonstrate that ASC-DRL improves model accu-racy by 89.2%and reduces training loss by 99.8%compared to traditional methods,while adaptively en-hancing the performance and stability of FL in dynamic environments.