# RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning

arXiv: https://arxiv.org/abs/2503.19886 · HTML: https://mlsanity.com/paper/2503.19886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization](https://mlsanity.com/paper/2102.02414/) (2021)
- [Mitigating Memorization of Noisy Labels by Clipping the Model Prediction](https://mlsanity.com/paper/2212.04055/) (2022)
- [Fundamentals of Task-Agnostic Data Valuation](https://mlsanity.com/paper/2208.12354/) (2022)
- [CTRL: Clustering Training Losses for Label Error Detection](https://mlsanity.com/paper/2208.08464/) (2022)
- [Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates](https://mlsanity.com/paper/2302.04851/) (2023)
- [Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning](https://mlsanity.com/paper/2410.02733/) (2024)

## Built on by (most cited first, 3 of 3)

- [FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning](https://mlsanity.com/paper/2604.19729/) (2026)
- [FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2604.20825/) (2026)
- [Eigenspace-Based Clustering for Personalized System Identification](https://mlsanity.com/paper/2606.20811/) (2026)

## Abstract (arXiv)

We address the problem of cluster identity estimation in a personalized federated learning (PFL) setting in which users aim to learn different personal models. The backbone of effective learning in such a setting is to cluster users into groups whose objectives are similar. A typical approach in the literature is to achieve this by training users' data on different proposed personal models and assign them to groups based on which model achieves the lowest value of the users' loss functions. This process is to be done iteratively until group identities converge. A key challenge in such a setting arises when users have noisy labeled data, which may produce misleading values of their loss functions, and hence lead to ineffective clustering. To overcome this challenge, we propose a label-agnostic data similarity-based clustering algorithm, coined RCC-PFL, with three main advantages: the cluster identity estimation procedure is independent from the training labels; it is a one-shot clustering algorithm performed prior to the training; and it requires fewer communication rounds and less computation compared to iterative-based clustering methods. We validate our proposed algorithm using various models and datasets and show that it outperforms multiple baselines in terms of average accuracy and variance reduction.