# FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure

arXiv: https://arxiv.org/abs/2010.06870 · HTML: https://mlsanity.com/paper/2010.06870/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 30 of 46)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2403.16460/) (2024)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2501.01850/) (2025)
- [Spectral Co-Distillation for Personalized Federated Learning](https://mlsanity.com/paper/2401.17124/) (2024)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning](https://mlsanity.com/paper/2410.18862/) (2024)
- [Interaction-Aware Gaussian Weighting for Clustered Federated Learning](https://mlsanity.com/paper/2502.03340/) (2025)
- [Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach](https://mlsanity.com/paper/2410.13602/) (2024)
- [Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity](https://mlsanity.com/paper/2603.21276/) (2026)
- [Federated Learning with Intermediate Representation Regularization](https://mlsanity.com/paper/2210.15827/) (2022)
- [Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies](https://mlsanity.com/paper/2310.05397/) (2023)
- [FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration](https://mlsanity.com/paper/2409.18356/) (2024)
- [FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences](https://mlsanity.com/paper/2501.13604/) (2025)
- [A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications](https://mlsanity.com/paper/2501.17512/) (2025)
- [Personalized Federated Learning via Learning Dynamic Graphs](https://mlsanity.com/paper/2503.05474/) (2025)
- [Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning](https://mlsanity.com/paper/2509.23683/) (2025)
- [Group Personalized Federated Learning](https://mlsanity.com/paper/2210.01863/) (2022)
- [Medical Federated Model with Mixture of Personalized and Sharing Components](https://mlsanity.com/paper/2306.14483/) (2023)
- [REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings](https://mlsanity.com/paper/2309.14088/) (2023)
- [Seamless Integration: Sampling Strategies in Federated Learning Systems](https://mlsanity.com/paper/2408.09545/) (2024)
- [Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees](https://mlsanity.com/paper/2410.08934/) (2024)
- [Federated Learning for Commercial Image Sources](https://mlsanity.com/paper/2507.12903/) (2025)
- [Clustered Federated Learning with Hierarchical Knowledge Distillation](https://mlsanity.com/paper/2512.10443/) (2025)
- [DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning](https://mlsanity.com/paper/2403.09284/) (2024)
- [Towards Client Driven Federated Learning](https://mlsanity.com/paper/2405.15407/) (2024)
- [Class-Wise Federated Averaging for Efficient Personalization](https://mlsanity.com/paper/2406.07800/) (2024)
- [CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering](https://mlsanity.com/paper/2407.12287/) (2024)

## Abstract (arXiv)

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced (statistical heterogeneity) training data of FL is distributed in the federated network, which will increase the divergences between the local models and global model, further degrading performance. In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure. 3) implement a newcomer device cold start mechanism based on the auxiliary global model for framework scalability and practicality. FedGroup can achieve improvements by dividing joint optimization into groups of sub-optimization and can be combined with FL optimizer FedProx. The convergence and complexity are analyzed to demonstrate the efficiency of our proposed framework. We also evaluate FedGroup and FedGrouProx (combined with FedProx) on several open datasets and made comparisons with related CFL frameworks. The results show that FedGroup can significantly improve absolute test accuracy by +14.1% on FEMNIST compared to FedAvg. +3.4% on Sentiment140 compared to FedProx, +6.9% on MNIST compared to FeSEM.