Hierarchical Over-the-Air Federated Learning with Differential Privacy
Abstract
Federated learning (FL) is a burgeoning field that examines the cooperative interaction of machine learning (ML) models with users, enabling the training of a global model while each user retains its data locally. With differential privacy (DP), FL also becomes an enabler for training ML models in a more private manner. While there has been a growing body of work exploring various aspects of FL, most studies, especially in the context of hierarchical federated learning (HFL), treat different levels of the hierarchy as a composition of two DP mechanisms. In this paper, we introduce a DP based privacy preserving method with hierarchical over-the-air FL and address both communication and privacy aspects in an end-to-end fashion.
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