mlsanityData to 7 Oct 2026

Conflict-Aware Client Selection for Multi-Server Federated Learning

arXiv 2602.02458 ↗First posted 2 Feb 2026cs.LG cs.NIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

24 of 34 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  6. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  7. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2021Federated Learning for Open Bankingcited by 138
  10. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  11. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
  12. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
12 more
  1. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  2. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
  3. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  4. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  5. 2024MERIT: Multimodal Wearable Vital Sign Waveform Monitoringcited by 16
  6. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  7. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  8. 2024LEO Satellite Networks Assisted Geo-distributed Data Processingcited by 10
  9. 2025PRISM: Privacy-Aware Routing for Adaptive Cloud-Edge LLM Inference via Semantic Sketch Collaborationcited by 10
  10. 2022On the Convergence of Multi-Server Federated Learning with Overlapping Areacited by 8
  11. 2025SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compressioncited by 6
  12. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
  2. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  3. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  4. 2026Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learningcited by 0
  5. 2026SAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigationcited by 0