Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Yitian Zhang, Yuming Tang, Zijian Zhang, Meng Li, Zhen Li, Salabat Khan, Huaping Chen and Guoqiang Cheng
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Yitian Zhang: School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
Yuming Tang: School of Cyberspace Science & Technology, Beijing Institute of Technology, Beijing 100081, China
Zijian Zhang: School of Cyberspace Science & Technology, Beijing Institute of Technology, Beijing 100081, China
Meng Li: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Zhen Li: School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
Salabat Khan: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Huaping Chen: Qianxin Technology Group Company, Beijing 100044, China
Guoqiang Cheng: Qianxin Technology Group Company, Beijing 100044, China

Mathematics, 2023, vol. 11, issue 5, 1-16

Abstract: Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

Keywords: blockchain; verifiable random functions; differential privacy; zero-knowledge proof; federated learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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