# Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review

arXiv: https://arxiv.org/abs/2506.02887 · HTML: https://mlsanity.com/paper/2506.02887/

## 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.

## Builds on (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [DELTA: Diverse Client Sampling for Fasting Federated Learning](https://mlsanity.com/paper/2205.13925/) (2022)
- [Convolutional Neural Network Training with Distributed K-FAC](https://mlsanity.com/paper/2007.00784/) (2020)
- [FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning](https://mlsanity.com/paper/2106.02310/) (2021)
- [Anchor Sampling for Federated Learning with Partial Client Participation](https://mlsanity.com/paper/2206.05891/) (2022)
- [Federated Learning with Regularized Client Participation](https://mlsanity.com/paper/2302.03662/) (2023)
- [Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback](https://mlsanity.com/paper/2112.14332/) (2021)
- [FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning](https://mlsanity.com/paper/2405.11811/) (2024)
- [Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection](https://mlsanity.com/paper/2201.05762/) (2022)
- [Combating Client Dropout in Federated Learning via Friend Model Substitution](https://mlsanity.com/paper/2205.13222/) (2022)
- [Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation](https://mlsanity.com/paper/2405.02745/) (2024)
- [Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training](https://mlsanity.com/paper/2406.15125/) (2024)
- [Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update](https://mlsanity.com/paper/2207.08391/) (2022)
- [Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning](https://mlsanity.com/paper/2412.11660/) (2024)
- [Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2502.00036/) (2025)
- [LEFL: Low Entropy Client Sampling in Federated Learning](https://mlsanity.com/paper/2312.17430/) (2023)
- [Semi-Variance Reduction for Fair Federated Learning](https://mlsanity.com/paper/2406.16193/) (2024)
- [Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learning](https://mlsanity.com/paper/2503.11151/) (2025)

## 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.