# Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

arXiv: https://arxiv.org/abs/2012.08009 · HTML: https://mlsanity.com/paper/2012.08009/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients](https://mlsanity.com/paper/2201.01601/) (2022)
- [Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://mlsanity.com/paper/2106.03328/) (2021)
- [DELTA: Diverse Client Sampling for Fasting Federated Learning](https://mlsanity.com/paper/2205.13925/) (2022)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [Study of the performance and scalability of federated learning for medical imaging with intermittent clients](https://mlsanity.com/paper/2207.08581/) (2022)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Sparsified Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2112.12872/) (2021)
- [FLAME: Federated Learning Across Multi-device Environments](https://mlsanity.com/paper/2202.08922/) (2022)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback](https://mlsanity.com/paper/2112.14332/) (2021)
- [A Comprehensive Survey On Client Selections in Federated Learning](https://mlsanity.com/paper/2311.06801/) (2023)
- [Multi-Model Federated Learning](https://mlsanity.com/paper/2201.02582/) (2022)
- [Network Anomaly Detection Using Federated Learning](https://mlsanity.com/paper/2303.07452/) (2023)
- [Multi-dimensional Fair Federated Learning](https://mlsanity.com/paper/2312.05551/) (2023)
- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection](https://mlsanity.com/paper/2201.05762/) (2022)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)
- [Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization](https://mlsanity.com/paper/2308.16889/) (2023)
- [Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance](https://mlsanity.com/paper/2310.02698/) (2023)
- [Seamless Integration: Sampling Strategies in Federated Learning Systems](https://mlsanity.com/paper/2408.09545/) (2024)
- [Intelligent Client Selection for Federated Learning using Cellular Automata](https://mlsanity.com/paper/2310.00627/) (2023)
- [FedEGG: Federated Learning with Explicit Global Guidance](https://mlsanity.com/paper/2404.11888/) (2024)
- [FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification](https://mlsanity.com/paper/2407.19103/) (2024)
- [Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning](https://mlsanity.com/paper/2506.14251/) (2025)
- [Tackling Privacy Heterogeneity in Differentially Private Federated Learning](https://mlsanity.com/paper/2602.22633/) (2026)
- [Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning](https://mlsanity.com/paper/2605.11815/) (2026)
- [Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning](https://mlsanity.com/paper/2605.20975/) (2026)

## Abstract (arXiv)

Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection has shown that selecting clients with higher local losses can improve error convergence speed. However, previously proposed biased selection strategies either require additional communication cost for evaluating the exact local loss or utilize stale local loss, which can even make the model diverge. In this paper, we present a bandit-based communication-efficient client selection strategy UCB-CS that achieves faster convergence with lower communication overhead. We also demonstrate how client selection can be used to improve fairness.