Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
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Abstract (arXiv)
Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw data from different clients. This paper presents an extensive survey on the impact of partial client participation in federated learning. While much of the existing research focuses on addressing issues such as generalization, robustness, and fairness caused by data heterogeneity under the assumption of full client participation, limited attention has been given to the practical and theoretical challenges arising from partial client participation, which is common in real-world scenarios. This survey provides an in-depth review of existing FL methods designed to cope with partial client participation. We offer a comprehensive analysis supported by theoretical insights and empirical findings, along with a structured categorization of these methods, highlighting their respective advantages and disadvantages.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 3 Jun 2025.
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References · 27
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
- 2022Federated Learning with Partial Model Personalizationcited by 114
- 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
- 2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 64
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
- 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
- 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
- 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
- 2020Convolutional Neural Network Training with Distributed K-FACcited by 22
15 more
- 2021FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learningcited by 14
- 2022Anchor Sampling for Federated Learning with Partial Client Participationcited by 14
- 2023Federated Learning with Regularized Client Participationcited by 14
- 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 7
- 2024FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learningcited by 5
- 2022Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selectioncited by 3
- 2022Combating Client Dropout in Federated Learning via Friend Model Substitutioncited by 3
- 2024Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participationcited by 3
- 2024Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Trainingcited by 3
- 2022Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Updatecited by 2
- 2024Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learningcited by 2
- 2025Efficient Client Selection in Federated Learningcited by 2
- 2023LEFL: Low Entropy Client Sampling in Federated Learningcited by 1
- 2024Semi-Variance Reduction for Fair Federated Learningcited by 1
- 2025Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learningcited by 1
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