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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.03862 |
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| _version_ | 1866915082900865024 |
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| author | Chowdhury, Sujit Halder, Raju |
| author_facet | Chowdhury, Sujit Halder, Raju |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03862 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning Chowdhury, Sujit Halder, Raju Machine 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 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. |
| title | FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.03862 |