FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning

Sujit Chowdhury; Raju Halder

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arXiv · 2407.03862

FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning

Sujit Chowdhury·Raju Halder

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Abstract

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning, but faces challenges with heterogeneous data distributions across clients. This paper presents FedSat, a novel FL approach specifically designed to simultaneously handle three forms of data heterogeneity, namely label skewness, missing classes, and quantity skewness, by proposing a prediction-sensitive loss function and a prioritized-class based weighted aggregation scheme. While the prediction-sensitive loss function enhances model performance on minority classes, the prioritized-class based weighted aggregation scheme ensures client contributions are weighted based on both statistical significance and performance on critical classes. Extensive experiments across diverse data-heterogeneity settings demonstrate that FedSat significantly outperforms state-of-the-art baselines, with an average improvement of 1.8% over the second-best method and 19.87% over the weakest-performing baseline. The approach also demonstrates faster convergence compared to existing methods. These results highlight FedSat's effectiveness in addressing the challenges of heterogeneous federated learning and its potential for real-world applications.

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BibTeX RIS

Sujit Chowdhury, Raju Halder. 2024-12-30. FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning. https://arxiv.org/abs/2407.03862

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