# FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

arXiv: https://arxiv.org/abs/2103.13822 · HTML: https://mlsanity.com/paper/2103.13822/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Overcoming Forgetting in Federated Learning on Non-IID Data](https://mlsanity.com/paper/1910.07796/) (2019)
- [Robust Federated Learning: The Case of Affine Distribution Shifts](https://mlsanity.com/paper/2006.08907/) (2020)
- [Variance Reduced Local SGD with Lower Communication Complexity](https://mlsanity.com/paper/1912.12844/) (2019)
- [Active Federated Learning](https://mlsanity.com/paper/1909.12641/) (2019)
- [LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets](https://mlsanity.com/paper/2008.03371/) (2020)
- [A Variational Analysis of Stochastic Gradient Algorithms](https://mlsanity.com/paper/1602.02666/) (2016)
- [Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning](https://mlsanity.com/paper/2102.03198/) (2021)

## Built on by (most cited first, 30 of 43)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models](https://mlsanity.com/paper/2310.01467/) (2023)
- [MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling](https://mlsanity.com/paper/2303.09373/) (2023)
- [Bayesian Federated Learning: A Survey](https://mlsanity.com/paper/2304.13267/) (2023)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Federated Learning under Heterogeneous and Correlated Client Availability](https://mlsanity.com/paper/2301.04632/) (2023)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [Exploiting Label Skews in Federated Learning with Model Concatenation](https://mlsanity.com/paper/2312.06290/) (2023)
- [Federated Learning over Coupled Graphs](https://mlsanity.com/paper/2301.11099/) (2023)
- [Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models](https://mlsanity.com/paper/2302.04464/) (2023)
- [FedUV: Uniformity and Variance for Heterogeneous Federated Learning](https://mlsanity.com/paper/2402.18372/) (2024)
- [Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process](https://mlsanity.com/paper/2206.06357/) (2022)
- [Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning](https://mlsanity.com/paper/2212.09483/) (2022)
- [Optimizing Federated Learning by Entropy-Based Client Selection](https://mlsanity.com/paper/2411.01240/) (2024)
- [FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning](https://mlsanity.com/paper/2508.18060/) (2025)
- [Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations](https://mlsanity.com/paper/2309.15499/) (2023)
- [Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning](https://mlsanity.com/paper/2310.00198/) (2023)
- [FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning](https://mlsanity.com/paper/2506.07581/) (2025)
- [Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering](https://mlsanity.com/paper/2508.12672/) (2025)
- [Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning](https://mlsanity.com/paper/2405.13584/) (2024)
- [Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control](https://mlsanity.com/paper/2504.05553/) (2025)
- [Energy-Efficient Federated Learning for AIoT using Clustering Methods](https://mlsanity.com/paper/2505.09704/) (2025)
- [Distribution-Controlled Client Selection to Improve Federated Learning Strategies](https://mlsanity.com/paper/2509.20877/) (2025)
- [FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices](https://mlsanity.com/paper/2209.03839/) (2022)
- [FedCliP: Federated Learning with Client Pruning](https://mlsanity.com/paper/2301.06768/) (2023)
- [Personalized Federated Learning with Contextual Modulation and Meta-Learning](https://mlsanity.com/paper/2312.15191/) (2023)
- [FLASH: Federated Learning Across Simultaneous Heterogeneities](https://mlsanity.com/paper/2402.08769/) (2024)
- [Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2402.15111/) (2024)
- [GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning](https://mlsanity.com/paper/2403.17833/) (2024)

## Abstract (arXiv)

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by 34%~ 99% and 26%~ 51% on FMNIST and CIFAR-10, respectively.