VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning
VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning
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DOI：
10.1109/tifs.2020.3043139
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发表时间：
2021-01-01
影响因子：
6.8
通讯作者：
Baker, Thar
中科院分区：
文献类型：
--
作者：
Guo, Xiaojie;Liu, Zheli;Baker, Thar
联合学习(FL)使大量客户能够通过在局部训练的每个同步时期共享它们的梯度来协作地训练全局模型。然而，用于聚合这些梯度的集中式服务器可能被破坏并伪造结果，以便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中设计通信高效和快速的可验证聚合。提出了一种可验证的聚集协议VERIFL，每个历元的通信次数为O(N)(维度无关)，验证运算量为O(N+d)，其中N为客户端数量，d为梯度向量的维度。由于d在一些真实的FL应用中可能很大(例如，100K)，因此我们的与维度无关的通信对于具有有限带宽和高维梯度的客户端尤其可取。此外，所提出的协议可用于需要安全聚合或存在退出协议执行的客户端的子集的FL设置中。实验结果表明，该协议在这些环境下是有效的。
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 VERIFL, 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.