Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

Lu, Yang and Yu, Zhengxin and Suri, Neeraj (2023) Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph. ACM Transactions on Privacy and Security, 26 (3): 33. pp. 1-39. ISSN 2471-2574

[thumbnail of 3591354]
Text (3591354)
3591354.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (2MB)


Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where participating learners and the communication graph between them may vary during the learning process. In particular, whenever the communication graph changes, the Metropolis-Hastings method [69] is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir’s secret sharing scheme [61] is integrated to facilitate privacy in reaching consensus of the global model. The paper establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-world dataset.

Item Type:
Journal Article
Journal or Publication Title:
ACM Transactions on Privacy and Security
Uncontrolled Keywords:
Research Output Funding/yes_externally_funded
?? privacymobilityfederated learningdecentralized aggregationyes - externally funded ??
ID Code:
Deposited By:
Deposited On:
10 May 2023 14:35
Last Modified:
09 May 2024 01:05