An Efficient Federated Learning Method Based on Optimized-residual and Clustering-Reference-Cited by-同舟云学术

An Efficient Federated Learning Method Based on Optimized-residual and Clustering

Published:2023-05-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 15th International Conference on Digital Image Processing
language:
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Author:

Wang Zhengyao¹^ORCID,Li Hongjiao¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Shanghai University of Electric Power, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604078.3604153

Reference12 articles.

1. B. McMahan , D. Ramage and R. Scientists , “ Federated learning: Collaborative machine learning without centralized training data .” https://ai.googleblog.com/ 2017 /04/federated-learning-collaborative.html B. McMahan, D. Ramage and R. Scientists, “Federated learning: Collaborative machine learning without centralized training data.” https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

2. Random sample consensus

3. Yang C and Guo H . 2022 A Method of Image Semantic Segmentation Based on PSPNet[J] . Mathematical Problems in Engineering , 2022 . Yang C and Guo H. 2022 A Method of Image Semantic Segmentation Based on PSPNet[J]. Mathematical Problems in Engineering, 2022.

4. Practical Private Aggregation in Federated Learning Against Inference Attack

5. McMahan B. Moore E. Ramage D. Hampson S. and y Arcas B. A. 2017 Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR. McMahan B. Moore E. Ramage D. Hampson S. and y Arcas B. A. 2017 Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.