Dynamic selectout and voting-based federated learning for enhanced medical image analysis
Federated learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed he...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ada0a6 |
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| Summary: | Federated learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed healthcare systems. When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. Local medical imaging datasets frequently show diversity in distribution and data imbalances. In certain situations, traditional FL techniques like FedProx and federated averaging, which depend on data size to weight contributions, might not be the optimal choice. In order to improve parameter aggregation and client selection unpredictability and increase the model’s adaptability to imbalanced and heterogeneous datasets, our proposed FedVoteNet model introduces SelectOut techniques based on voting methodology. Based on how much their local performance has improved from the last communication cycle, we arbitrarily remove clients. Additionally eliminated are clients whose model weights when combined with the global model adversely affect its performance. Our method is further enhanced by the inclusion of a voting mechanism. At the conclusion of each communication cycle, clients that improve both their local performance and their contribution to the global model are awarded higher voting values. This encourages more significant and effective contributions from clients by providing incentives for them to actively increase the diversity of their training data. We assess our approach on a dataset of medical images, including magnetic resonance imaging scans, and find that the FL model performs noticeably better (F1 Score = 0.968, Sensitivity = 0.977, Specificity = 0.945, and AUC = 0.950). The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. To sum up, our proposed approach uses voting and dynamic SelectOut techniques to improve FL performance on a variety of uneven, distributed, and varied datasets. This strategy has a lot of potential to improve FL across a range of applications, especially those that prioritize data privacy, diversity, and performance. |
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| ISSN: | 2632-2153 |