Conflict-Aware Client Selection for Multi-Server Federated Learning

• URL: http://arxiv.org/abs/2602.02458v1
• Date: Mon, 02 Feb 2026 18:47:16 GMT
• Title: Conflict-Aware Client Selection for Multi-Server Federated Learning
• Authors: Mingwei Hong, Zheng Lin, Zehang Lin, Lin Li, Miao Yang, Xia Du, Zihan Fang, Zhaolu Kang, Dianxin Luan, Shunzhi Zhu,
• Abstract summary: Federated learning (FL) enables collaborative model training across clients without exposing raw data.<n>Traditional single-server FL suffers from high communication latency due to the aggregation of models from a large number of clients.<n>We propose a decentralized reinforcement learning with conflict risk prediction, named RL CRP, to optimize client selection in multi-server FL systems.
• Score: 17.73110381901509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has emerged as a promising distributed machine learning (ML) that enables collaborative model training across clients without exposing raw data, thereby preserving user privacy and reducing communication costs. Despite these benefits, traditional single-server FL suffers from high communication latency due to the aggregation of models from a large number of clients. While multi-server FL distributes workloads across edge servers, overlapping client coverage and uncoordinated selection often lead to resource contention, causing bandwidth conflicts and training failures. To address these limitations, we propose a decentralized reinforcement learning with conflict risk prediction, named RL CRP, to optimize client selection in multi-server FL systems. Specifically, each server estimates the likelihood of client selection conflicts using a categorical hidden Markov model based on its sparse historical client selection sequence. Then, a fairness-aware reward mechanism is incorporated to promote long-term client participation for minimizing training latency and resource contention. Extensive experiments demonstrate that the proposed RL-CRP framework effectively reduces inter-server conflicts and significantly improves training efficiency in terms of convergence speed and communication cost.

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