Title
PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework
Abstract
Over the past years, the increasingly severe data island problem has spawned an emerging distributed deep learning framework-federated learning, in which the global model can be constructed over multiple participants without directly sharing their raw data. Despite its promising prospect, there are still many security challenges in federated learning, such as privacy preservation and integrity verification. Furthermore, federated learning is usually performed with the assistance of a center, which is prone to cause trust worries and communicational bottlenecks. To tackle these challenges, in this paper, we propose a privacy-preserving and verifiable decentralized federated learning framework, named PVD-FL, which can achieve secure deep learning model training under a decentralized architecture. Specifically, we first design an efficient and verifiable cipher-based matrix multiplication (EVCM) algorithm to execute the most basic calculation in deep learning. Then, by employing EVCM, we design a suite of decentralized algorithms to construct the PVD-FL framework, which ensures the confidentiality of both global model and local update and the verification of every training step. Detailed security analysis shows that PVD-FL can well protect privacy against various inference attacks and guarantee training integrity. In addition, the extensive experiments on real-world datasets also demonstrate that PVD-FL can achieve lossless accuracy and practical performance.
Year
DOI
Venue
2022
10.1109/TIFS.2022.3176191
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Keywords
DocType
Volume
Computational modeling, Training, Data models, Security, Data privacy, Privacy, Deep learning, Federated learning, privacy-preserving, verification, decentralized, efficiency
Journal
17
ISSN
Citations 
PageRank 
1556-6013
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jiaqi Zhao100.34
Hui Zhu28317.00
Fengwei Wang393.51
Rongxing Lu45091301.87
Zhe Liu528754.56
Hui Li620234.25