{"ID":2825848,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20363","arxiv_id":"2512.20363","title":"Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning","abstract":"Federated learning (FL) supports privacy-preserving, decentralized machine learning (ML) model training by keeping data on client devices. However, non-independent and identically distributed (non-IID) data across clients biases updates and degrades performance. To alleviate these issues, we propose Clust-PSI-PFL, a clustering-based personalized FL framework that uses the Population Stability Index (PSI) to quantify the level of non-IID data. We compute a weighted PSI metric, $WPSI^L$, which we show to be more informative than common non-IID metrics (Hellinger, Jensen-Shannon, and Earth Mover's distance). Using PSI features, we form distributionally homogeneous groups of clients via K-means++; the number of optimal clusters is chosen by a systematic silhouette-based procedure, typically yielding few clusters with modest overhead. Across six datasets (tabular, image, and text modalities), two partition protocols (Dirichlet with parameter $α$ and Similarity with parameter S), and multiple client sizes, Clust-PSI-PFL delivers up to 18% higher global accuracy than state-of-the-art baselines and markedly improves client fairness by a relative improvement of 37% under severe non-IID data. These results establish PSI-guided clustering as a principled, lightweight mechanism for robust PFL under label skew.","short_abstract":"Federated learning (FL) supports privacy-preserving, decentralized machine learning (ML) model training by keeping data on client devices. However, non-independent and identically distributed (non-IID) data across clients biases updates and degrades performance. To alleviate these issues, we propose Clust-PSI-PFL, a cl...","url_abs":"https://arxiv.org/abs/2512.20363","url_pdf":"https://arxiv.org/pdf/2512.20363v2","authors":"[\"Daniel M. Jimenez-Gutierrez\",\"Mehrdad Hassanzadeh\",\"David Solans\",\"Mohammed Elbamby\",\"Nicolas Kourtellis\",\"Aris Anagnostopoulos\",\"Ioannis Chatzigiannakis\",\"Andrea Vitaletti\"]","published":"2025-12-23T13:46:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.DC\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}