Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network

Ningyu An; Xiao Liang; Fei Zhou; Xiaohui Wang; Zihan Li; Jia Feng; Zhitao Guan

doi:10.1007/978-981-99-8101-4_7

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Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network

Ningyu An Xiao Liang Fei Zhou Xiaohui Wang Zihan Li Jia Feng Zhitao Guan

Year: 2023 Communications in computer and information science Pages: 91-104 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-99-8101-4_7

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Keywords:

Computer science Homomorphic encryption Asynchronous communication Scheme (mathematics) Distributed computing Encryption Overhead (engineering) Enhanced Data Rates for GSM Evolution Distributed learning Edge device Computer network Artificial intelligence Cloud computing

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Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Cryptography and Data Security

Physical Sciences → Computer Science → Artificial Intelligence

Privacy, Security, and Data Protection

Social Sciences → Social Sciences → Sociology and Political Science

Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network

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