TY - JOUR
T1 - BPFL
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
AU - Wang, Naiyu
AU - Yang, Wenti
AU - Guan, Zhitao
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Federated 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.
AB - Federated 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.
KW - Blockchain
KW - Federated Learning
KW - Homomorphic Encryption
KW - Privacy-Preservation
UR - http://www.scopus.com/inward/record.url?scp=85184654265&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184654265&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM46510.2021.9685821
DO - 10.1109/GLOBECOM46510.2021.9685821
M3 - Conference article
AN - SCOPUS:85184654265
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
Y2 - 7 December 2021 through 11 December 2021
ER -