Enhancing Differential Privacy for Federated Learning at Scale

IEEE Access

Sungwook Kim¹  Chunghun Baek²  Jihoon Park²  Dongkyun Nam³ 

[1] Department of Information Security, Seoul Women&x2019;Samsung Research, Seoul, South Korea;s University, Seoul, South Korea;

关键词: Differential privacy;    federated learning;    user dropout;    noise calibration;   

DOI  :  10.1109/ACCESS.2021.3124020

来源: DOAJ

【 摘 要 】

Federated learning (FL) is an emerging technique that trains machine learning models across multiple de-centralized systems. It enables local devices to collaboratively learn a model by aggregating locally computed updates via a server. Privacy is a core aspect of FL, and recent works in this area are advancing the privacy guarantee of an FL network. To ensure rigorous privacy guarantee for FL, prior works have focused on methods to securely aggregate local updates and provide differential privacy (DP). In this paper, we investigate a new privacy risk for FL. Specifically, FL may frequently encounter unexpected user dropouts because it is implemented over a large-scale network. We first observe that user dropouts of an FL network may lead to failure in achieving the desired level of privacy protection, i.e., over-consumption of the privacy budget. Subsequently, we develop a DP mechanism robust to user dropouts by dynamically calibrating noise with account of the dropout rate. We evaluate the proposed technique to train convolutional neural network models on MNIST and FEMNIST datasets over a simulated FL network. Our results show that our approach significantly improves privacy guarantee for user dropouts compared to existing DP algorithms on FL networks.

【 授权许可】

Unknown