# FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering

arXiv: https://arxiv.org/abs/2403.04144 · HTML: https://mlsanity.com/paper/2403.04144/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (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)
- [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)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces](https://mlsanity.com/paper/2209.10526/) (2022)
- [PGFed: Personalize Each Client's Global Objective for Federated Learning](https://mlsanity.com/paper/2212.01448/) (2022)
- [FedFair^3: Unlocking Threefold Fairness in Federated Learning](https://mlsanity.com/paper/2401.16350/) (2024)

## Built on by (most cited first, 9 of 9)

- [FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering](https://mlsanity.com/paper/2407.07124/) (2024)
- [Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning](https://mlsanity.com/paper/2508.05224/) (2025)
- [Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions](https://mlsanity.com/paper/2407.04302/) (2024)
- [FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting](https://mlsanity.com/paper/2504.20282/) (2025)
- [FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization](https://mlsanity.com/paper/2505.02515/) (2025)
- [Clustered Federated Learning via Embedding Distributions](https://mlsanity.com/paper/2506.07769/) (2025)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)
- [Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning](https://mlsanity.com/paper/2608.02250/) (2026)
- [Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning](https://mlsanity.com/paper/2608.12108/) (2026)

## Abstract (arXiv)

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.