# Conflict-Aware Client Selection for Multi-Server Federated Learning

arXiv: https://arxiv.org/abs/2602.02458 · HTML: https://mlsanity.com/paper/2602.02458/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2505.02795/) (2025)
- [FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2403.16460/) (2024)
- [SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework](https://mlsanity.com/paper/2409.13503/) (2024)
- [MERIT: Multimodal Wearable Vital Sign Waveform Monitoring](https://mlsanity.com/paper/2410.00392/) (2024)
- [LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2501.01850/) (2025)
- [HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts](https://mlsanity.com/paper/2601.00583/) (2026)
- [LEO Satellite Networks Assisted Geo-distributed Data Processing](https://mlsanity.com/paper/2406.10856/) (2024)
- [PRISM: Privacy-Aware Routing for Adaptive Cloud-Edge LLM Inference via Semantic Sketch Collaboration](https://mlsanity.com/paper/2511.22788/) (2025)
- [On the Convergence of Multi-Server Federated Learning with Overlapping Area](https://mlsanity.com/paper/2208.07893/) (2022)
- [SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression](https://mlsanity.com/paper/2508.12984/) (2025)
- [LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.14680/) (2025)

## Built on by (most cited first, 5 of 5)

- [Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity](https://mlsanity.com/paper/2603.21276/) (2026)
- [SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression](https://mlsanity.com/paper/2604.07316/) (2026)
- [Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification](https://mlsanity.com/paper/2604.14643/) (2026)
- [Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learning](https://mlsanity.com/paper/2603.09792/) (2026)
- [SAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation](https://mlsanity.com/paper/2609.16601/) (2026)

## 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.