VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning

Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on information forensics and security 2021, Vol.16, p.1736-1751
Main Authors: Guo, Xiaojie, Liu, Zheli, Li, Jin, Gao, Jiqiang, Hou, Boyu, Dong, Changyu, Baker, Thar
Format: Article
Language:English
Subjects:
Bandwidth
Collaborative work
commitment
Federated learning
linearly homomorphic hash
machine learning
Privacy
Protocols
Servers
Synchronization
Training
verifiable aggregation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate privacy or launch other attacks, which incurs the need to verify the integrity of aggregation. In this work, we explore how to design communication-efficient and fast verifiable aggregation in FL. We propose V eri FL, a verifiable aggregation protocol, with O(N) (dimension-independent) communication and O(N+ d) computation for verification in each epoch, where N is the number of clients and d is the dimension of gradient vectors. Since d can be large in some real-world FL applications (e.g., 100K), our dimension-independent communication is especially desirable for clients with limited bandwidth and high-dimensional gradients. In addition, the proposed protocol can be used in the FL setting where secure aggregation is needed or there is a subset of clients dropping out of protocol execution. Experimental results indicate that our protocol is efficient in these settings.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2020.3043139