FedSat: A Statistical Aggregation Approach for Class Imbalaced Clients in Federated Learning
<|reference_start|>FedSat: A Statistical Aggregation Approach for Class Imbalaced Clients in Federated Learning: 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.<|reference_end|>
@article{chowdhury2024fedsat:,
    title={FedSat: A Statistical Aggregation Approach for Class Imbalaced Clients
  in Federated Learning},
    author={Sujit Chowdhury, Raju Halder},
    journal={arXiv preprint arXiv:2407.03862},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03862},
    primaryClass={cs.LG}
}