Toward Secure and Robust Federated Distillation in Distributed Cloud: Challenges and Design Issues

Author:

Wang Xiaodong1ORCID,Guan Zhitao1ORCID,Wu Longfei2ORCID,Gai Keke3ORCID

Affiliation:

1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

2. Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC, USA

3. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference15 articles.

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

2. Federated Edge Learning: Design Issues and Challenges

3. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data;Jeong

4. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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1. LRPAFL: Layer-Wise Relevance Propagation-Based Adaptive Federated Learning;2024 IEEE 11th International Conference on Cyber Security and Cloud Computing (CSCloud);2024-06-28

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