Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908845530415104 |
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| author | Jimenez-Gutierrez, Daniel M. Hassanzadeh, Mehrdad Solans, David Elbamby, Mohammed Kourtellis, Nicolas Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea |
| author_facet | Jimenez-Gutierrez, Daniel M. Hassanzadeh, Mehrdad Solans, David Elbamby, Mohammed Kourtellis, Nicolas Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning Jimenez-Gutierrez, Daniel M. Hassanzadeh, Mehrdad Solans, David Elbamby, Mohammed Kourtellis, Nicolas Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Applications 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. |
| title | Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Applications |
| url | https://arxiv.org/abs/2512.20363 |