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: Leiming Chen, Weishan Zhang, Cihao Dong, Dehai Zhao, Xingjie Zeng, Sibo Qiao, Yichang Zhu, Chee Wei Tan
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Entropy
Subjects:
heterogeneous federated learning
adaptive knowledge distillation
malicious client identification
trustworthy knowledge aggregation
Online Access:https://www.mdpi.com/1099-4300/26/1/96

Internet

https://www.mdpi.com/1099-4300/26/1/96

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