Federated learning with improved aggregation via optimal transport algorithm

Dawei Chen; Lu Yuan

doi:10.1117/12.3051407

18 November 2024 Federated learning with improved aggregation via optimal transport algorithm

Dawei Chen, Lu Yuan

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030S (2024) https://doi.org/10.1117/12.3051407
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

With the increasing awareness of privacy protection, federated learning is increasingly applied to distributed training scenarios. However, in the process of executing federated learning on distributed clients, due to the non-independent and identical distribution between clients, the accuracy and convergence speed of the global model will decrease in the model aggregation process of ordinary federated learning. To solve this problem, a federated learning framework is proposed to improve the model aggregation method. During training, the server extracts the feature parameters of the local model, and performs entropy regularization on each layer of the local model to obtain the optimal transmission feature parameters. Finally, the optimal transmission and other federated learning global model feature parameters are generated by fusion. In the distributed training of the two data sets, the data distribution in four different cases was simulated for federated training comparison. The results show that compared with ordinary federated learning, the proposed algorithm significantly improves the model performance and the convergence speed of the global model, which can be used for distributed user training and commercial privacy-sensitive scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dawei Chen and Lu Yuan "Federated learning with improved aggregation via optimal transport algorithm", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030S (18 November 2024); https://doi.org/10.1117/12.3051407

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