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AuthorWang, Naiyu
AuthorYang, Wenti
AuthorGuan, Zhitao
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-09T20:56:53Z
Publication Date2021-01-01
Publication Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
Identifierhttp://dx.doi.org/10.1109/GLOBECOM46510.2021.9685821
CitationWang, N., Yang, W., Guan, Z., Du, X., & Guizani, M. (2021, December). Bpfl: A blockchain based privacy-preserving federated learning scheme. In 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.‏
ISBN9781728181042
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127273099&origin=inward
URIhttp://hdl.handle.net/10576/35988
AbstractFederated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.
SponsorThis work is supported by National Foundation of China (61972148).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBlockchain
Federated Learning
Homomorphic Encryption
Privacy-Preservation
TitleBPFL: A Blockchain Based Privacy-Preserving Federated Learning Scheme
TypeConference Paper
dc.accessType Abstract Only


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