To be global or personalized: Generalized federated learning with cooperative adaptation for data heterogeneity

Author:

Ding KaijianORCID,Feng XiangORCID,Yu HuiqunORCID

Funder

Key Research and Development Program of Zhejiang Province

National Defense Basic Scientific Research Program of China

National Aerospace Science Foundation of China

Special funds of Guangdong Province for Promoting Economic Development

Shanghai Municipal Key Laboratory of Intelligent Information Processing, Science and Technology Commission of Shanghai Municipality

Publisher

Elsevier BV

Reference70 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

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3. An efficient federated distillation learning system for multitask time series classification;Xing;IEEE Trans. Instrum. Meas.,2022

4. Hierarchical clustering-based personalized federated learning for robust and fair human activity recognition;Li;Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.,2023

5. Partial domain adaptation in remaining useful life prediction with incomplete target data;Li;IEEE/ASME Trans. Mechatronics,2023

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