# Stochastic Client Selection for Federated Learning with Volatile Clients

arXiv: https://arxiv.org/abs/2011.08756 · HTML: https://mlsanity.com/paper/2011.08756/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 30 of 39)

- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training](https://mlsanity.com/paper/2206.00187/) (2022)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape](https://mlsanity.com/paper/2305.11584/) (2023)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [DELTA: Diverse Client Sampling for Fasting Federated Learning](https://mlsanity.com/paper/2205.13925/) (2022)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Fusion of Global and Local Knowledge for Personalized Federated Learning](https://mlsanity.com/paper/2302.11051/) (2023)
- [AdaFed: Fair Federated Learning via Adaptive Common Descent Direction](https://mlsanity.com/paper/2401.04993/) (2024)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [A Survey on Participant Selection for Federated Learning in Mobile Networks](https://mlsanity.com/paper/2207.03681/) (2022)
- [Federated Retrieval Augmented Generation for Multi-Product Question Answering](https://mlsanity.com/paper/2501.14998/) (2025)
- [Improving Accelerated Federated Learning with Compression and Importance Sampling](https://mlsanity.com/paper/2306.03240/) (2023)
- [ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme](https://mlsanity.com/paper/2211.02906/) (2022)
- [FedABC: Targeting Fair Competition in Personalized Federated Learning](https://mlsanity.com/paper/2302.07450/) (2023)
- [Distributed Quasi-Newton Method for Fair and Fast Federated Learning](https://mlsanity.com/paper/2501.10877/) (2025)
- [Efficient Node Selection in Private Personalized Decentralized Learning](https://mlsanity.com/paper/2301.12755/) (2023)
- [Asynchronous Wireless Federated Learning with Probabilistic Client Selection](https://mlsanity.com/paper/2311.16741/) (2023)
- [FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training](https://mlsanity.com/paper/2211.10948/) (2022)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)
- [FedFQ: Federated Learning with Fine-Grained Quantization](https://mlsanity.com/paper/2408.08977/) (2024)
- [Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems](https://mlsanity.com/paper/2406.04702/) (2024)
- [PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning](https://mlsanity.com/paper/2503.13173/) (2025)
- [VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Security](https://mlsanity.com/paper/2508.07960/) (2025)
- [FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices](https://mlsanity.com/paper/2601.17713/) (2026)
- [Tackling Computational Heterogeneity in FL: A Few Theoretical Insights](https://mlsanity.com/paper/2307.06283/) (2023)
- [Intelligent Client Selection for Federated Learning using Cellular Automata](https://mlsanity.com/paper/2310.00627/) (2023)

## Abstract (arXiv)

Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a fraction of available clients are chosen to participate, and the selection decision might have a significant effect on the training efficiency, as well as the final model performance. In this paper, we investigate the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency. colorblackIntuitively, too much training failure might potentially reduce the training efficiency, while too much selection on clients with greater stability might introduce bias, thereby resulting in degradation of the training effectiveness. To tackle this tradeoff, we in this paper formulate the client selection problem under joint consideration of effective participation and fairness. Further, we propose E3CS, a stochastic client selection scheme to solve the problem, and we corroborate its effectiveness by conducting real data-based experiments. According to our experimental results, the proposed selection scheme is able to achieve up to 2x faster convergence to a fixed model accuracy while maintaining the same level of final model accuracy, compared with the state-of-the-art selection schemes.