Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data

• URL: http://arxiv.org/abs/2505.02540v1
• Date: Mon, 05 May 2025 10:26:35 GMT
• Title: Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
• Authors: Ljubomir Rokvic, Panayiotis Danassis, Boi Faltings,
• Abstract summary: In Federated Learning, a single global model does not have the best performance for individual clients.<n>We propose a personalized federated learning framework (pFedLIA) that utilizes a computationally efficient influence approximation.<n>Our method has been shown to successfully recover the global model's performance drop due to the non-IID-Lazyness in various synthetic and real-world settings.
• Score: 15.15596911693489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for example training a next-word prediction model for keyboards: user-specific language patterns due to demographics (dialect, age, etc.), language proficiency, and writing style result in a highly non-IID dataset across clients. Other examples are medical images taken with different machines, or driving data from different vehicle types. To address this, we propose a simple yet effective personalized federated learning framework (pFedLIA) that utilizes a computationally efficient influence approximation, called `Lazy Influence', to cluster clients in a distributed manner before model aggregation. Within each cluster, data owners collaborate to jointly train a model that captures the specific data patterns of the clients. Our method has been shown to successfully recover the global model's performance drop due to the non-IID-ness in various synthetic and real-world settings, specifically a next-word prediction task on the Nordic languages as well as several benchmark tasks. It matches the performance of a hypothetical Oracle clustering, and significantly improves on existing baselines, e.g., an improvement of 17% on CIFAR100.

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