File(s) not publicly available
LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing
journal contribution
posted on 2023-02-20, 22:17 authored by Z Zhang, L Wu, C Ma, Jianxin LiJianxin Li, J Wang, Q Wang, S YuNowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.
History
Journal
IEEE Transactions on Information Forensics and SecurityVolume
18Pagination
365-379Publisher DOI
ISSN
1556-6013eISSN
1556-6021Language
EnglishPublication classification
C1 Refereed article in a scholarly journalPublisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC