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Personalized Cross-Silo Federated Learning on Non-IID Data

Publication ,  Conference
Huang, Y; Chu, L; Zhou, Z; Wang, L; Liu, J; Pei, J; Zhang, Y
Published in: 35th AAAI Conference on Artificial Intelligence, AAAI 2021
January 1, 2021

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.

Duke Scholars

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

Publication Date

January 1, 2021

Volume

9A

Start / End Page

7865 / 7873
 

Citation

APA
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Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., & Zhang, Y. (2021). Personalized Cross-Silo Federated Learning on Non-IID Data. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 9A, pp. 7865–7873).
Huang, Y., L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y. Zhang. “Personalized Cross-Silo Federated Learning on Non-IID Data.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 9A:7865–73, 2021.
Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, et al. Personalized Cross-Silo Federated Learning on Non-IID Data. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 7865–73.
Huang, Y., et al. “Personalized Cross-Silo Federated Learning on Non-IID Data.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 9A, 2021, pp. 7865–73.
Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y. Personalized Cross-Silo Federated Learning on Non-IID Data. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 7865–7873.

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

Publication Date

January 1, 2021

Volume

9A

Start / End Page

7865 / 7873