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VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning
IEEE Transactions on Information Forensics and Security ( IF 6.8 ) Pub Date : 2020-12-07 , DOI: 10.1109/tifs.2020.3043139
Xiaojie Guo , Zheli Liu , Jin Li , Jiqiang Gao , Boyu Hou , Changyu Dong , Thar Baker

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.

中文翻译:

V埃里FL:高效沟通的快速可验证聚合,用于联合学习

联合学习(FL)使大量客户可以通过在本地培训的每个同步纪元中共享其梯度来协作培训全局模型。但是,用于聚合这些梯度的集中式服务器可能会受到损害并伪造结果,以侵犯隐私或发起其他攻击,这导致需要验证聚合的完整性。在这项工作中,我们探索了如何在FL中设计高效通信和快速可验证的聚合。我们提议V 埃里 FL,一种可验证的聚合协议,具有 $ O(N)$ (与维度无关)的交流和 $ O(N + d)$ 在每个时期进行验证的计算,其中 $ N $ 是客户数量, $ d $ 是梯度向量的维数。以来 $ d $ 在某些现实世界的FL应用程序中(例如100K)可能很大,对于带宽有限和高维度渐变的客户端,我们特别希望进行尺寸无关的通信。此外,可以在需要安全聚合或客户端子集退出协议执行的FL设置中使用建议的协议。实验结果表明,我们的协议在这些情况下是有效的。
更新日期:2021-01-01
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