计算机科学
可验证秘密共享
正确性
架空(工程)
边缘计算
云计算
上传
GSM演进的增强数据速率
计算
分布式计算
理论计算机科学
人工智能
算法
操作系统
集合(抽象数据类型)
程序设计语言
作者
Hao Zhou,Geng Yang,Yuxian Huang,Hua Dai,Yang Xiang
标识
DOI:10.1109/tifs.2022.3227435
摘要
In federated learning (FL), each client collaboratively trains the global model through the cloud server (CS) without sharing its original dataset in edge computing. However, CS can analyze and forge the uploaded parameters and infer the privacy of clients, which calls for the necessity of verifying the integrity and protecting the privacy for aggregation. Although there are some works to ensure the verifiability of aggregation results, there is still a lack of work on analyzing the relationship between verification and dropout rate for edge computing. In this work, we propose privacy-preserving and verifiable federated learning (PVFL) with low communication and computation overhead for verification. We theoretically demonstrate that PVFL has three properties: 1) the communication overhead for verification is independent of the dropouts and the dimension of the parameter vector; 2) the computation overhead for verification is independent of the dropouts; 3) the value of the loss function is negatively correlated with the number of dropouts. Experimental results demonstrate the correctness of our theoretical results and practical performance with a high dropout rate, thereby facilitating the design of privacy-preserving and verifiable FL algorithms for edge computing with a high dimension of parameter vectors and a high dropout rate.
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