# Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions

arXiv: https://arxiv.org/abs/2407.04302 · HTML: https://mlsanity.com/paper/2407.04302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 87 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Distributionally Robust Federated Averaging](https://mlsanity.com/paper/2102.12660/) (2021)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://mlsanity.com/paper/1903.11260/) (2019)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [Federated Transfer Learning for EEG Signal Classification](https://mlsanity.com/paper/2004.12321/) (2020)
- [Personalized Federated Learning under Mixture of Distributions](https://mlsanity.com/paper/2305.01068/) (2023)
- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling](https://mlsanity.com/paper/2108.09412/) (2021)
- [SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision](https://mlsanity.com/paper/2110.02470/) (2021)
- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [Federated Learning without Full Labels: A Survey](https://mlsanity.com/paper/2303.14453/) (2023)
- [Secure Federated Clustering](https://mlsanity.com/paper/2205.15564/) (2022)
- [FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering](https://mlsanity.com/paper/2403.04144/) (2024)
- [Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data](https://mlsanity.com/paper/2105.04727/) (2021)
- [Privacy-Preserving Federated Deep Clustering based on GAN](https://mlsanity.com/paper/2211.16965/) (2022)
- [Federated clustering with GAN-based data synthesis](https://mlsanity.com/paper/2210.16524/) (2022)
- [UPFL: Unsupervised Personalized Federated Learning towards New Clients](https://mlsanity.com/paper/2307.15994/) (2023)
- [CCFC++: Enhancing Federated Clustering through Feature Decorrelation](https://mlsanity.com/paper/2402.12852/) (2024)
- [FedSSC: Shared Supervised-Contrastive Federated Learning](https://mlsanity.com/paper/2301.05797/) (2023)
- [EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence](https://mlsanity.com/paper/2111.03265/) (2021)
- [Neural Network Based Epileptic EEG Detection and Classification](https://mlsanity.com/paper/2111.03268/) (2021)
- [Automated Human Mind Reading Using EEG Signals for Seizure Detection](https://mlsanity.com/paper/2111.03270/) (2021)

## Built on by (most cited first, 1 of 1)

- [Topological Federated Clustering via Gravitational Potential Fields under Local Differential Privacy](https://mlsanity.com/paper/2512.00849/) (2025)

## Abstract (arXiv)

The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into k partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite ℓ-norm, while simultaneously achieving lower cost and variance.