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

• URL: http://arxiv.org/abs/2407.03862v1
• Date: Thu, 4 Jul 2024 11:50:24 GMT
• Title: FedSat: A Statistical Aggregation Approach for Class Imbalaced Clients in Federated Learning
• Authors: Sujit Chowdhury, Raju Halder,
• Abstract summary: Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning. This paper introduces FedSat, a novel FL approach designed to tackle various forms of data heterogeneity simultaneously.
• Score: 2.5628953713168685
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 introduces FedSat, a novel FL approach designed to tackle various forms of data heterogeneity simultaneously. FedSat employs a cost-sensitive loss function and a prioritized class-based weighted aggregation scheme to address label skewness, missing classes, and quantity skewness across clients. While the proposed cost-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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