Robust Federated Clustering under Heterogeneity and Adversaries

Martín Bravo, Sebastian Dalleiger
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:3718-3726, 2026.

Abstract

Clustering distributed and private data is an increasingly important task across domains that handle sensitive information, such as life sciences and clinical research. In federated settings, clustering faces three challenges: heterogeneous client data distributions, adversarial behavior, and strict privacy requirements. Existing approaches often exhibit significant performance degradation under these conditions and fail to return accurate solutions. To overcome these limitations, we introduce a novel federated clustering algorithm that combines client-side differential privacy with Byzantine-robust aggregation at the server, based on a novel efficient and robust clustering procedure. Our method comes with theoretical robustness guarantees, and through extensive experiments on synthetic and real-world data, we demonstrate that it produces high-quality clusters in just a few communication rounds, even in scenarios where state-of-the-art methods fail.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-bravo26a, title = { Robust Federated Clustering under Heterogeneity and Adversaries }, author = {Bravo, Mart{\'i}n and Dalleiger, Sebastian}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {3718--3726}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/bravo26a/bravo26a.pdf}, url = {https://proceedings.mlr.press/v300/bravo26a.html}, abstract = { Clustering distributed and private data is an increasingly important task across domains that handle sensitive information, such as life sciences and clinical research. In federated settings, clustering faces three challenges: heterogeneous client data distributions, adversarial behavior, and strict privacy requirements. Existing approaches often exhibit significant performance degradation under these conditions and fail to return accurate solutions. To overcome these limitations, we introduce a novel federated clustering algorithm that combines client-side differential privacy with Byzantine-robust aggregation at the server, based on a novel efficient and robust clustering procedure. Our method comes with theoretical robustness guarantees, and through extensive experiments on synthetic and real-world data, we demonstrate that it produces high-quality clusters in just a few communication rounds, even in scenarios where state-of-the-art methods fail. } }
Endnote
%0 Conference Paper %T Robust Federated Clustering under Heterogeneity and Adversaries %A Martín Bravo %A Sebastian Dalleiger %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-bravo26a %I PMLR %P 3718--3726 %U https://proceedings.mlr.press/v300/bravo26a.html %V 300 %X Clustering distributed and private data is an increasingly important task across domains that handle sensitive information, such as life sciences and clinical research. In federated settings, clustering faces three challenges: heterogeneous client data distributions, adversarial behavior, and strict privacy requirements. Existing approaches often exhibit significant performance degradation under these conditions and fail to return accurate solutions. To overcome these limitations, we introduce a novel federated clustering algorithm that combines client-side differential privacy with Byzantine-robust aggregation at the server, based on a novel efficient and robust clustering procedure. Our method comes with theoretical robustness guarantees, and through extensive experiments on synthetic and real-world data, we demonstrate that it produces high-quality clusters in just a few communication rounds, even in scenarios where state-of-the-art methods fail.
APA
Bravo, M. & Dalleiger, S.. (2026). Robust Federated Clustering under Heterogeneity and Adversaries . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:3718-3726 Available from https://proceedings.mlr.press/v300/bravo26a.html.

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