FedTKD: a trustworthy heterogeneous federated learning based on adaptive knowledge distillation

FedTKD: a trustworthy heterogeneous federated learning based on adaptive knowledge distillation

Federated learning allows multiple parties to train models while jointly protecting user privacy. However, traditional federated learning requires each client to have the same model structure to fuse the global model. In real-world scenarios, each client may need to develop personalized models based...

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Bibliographic Details
Main Authors: Chen, Leiming, Zhang, Weishan, Dong, Cihao, Zhao, Dehai, Zeng, Xingjie, Qiao, Sibo, Zhu, Yichang, Tan, Chee Wei
Other Authors: School of Civil and Environmental Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Malicious client identification
Knowledge distillation
Online Access:https://hdl.handle.net/10356/174735

Internet

https://hdl.handle.net/10356/174735

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