Hierarchical Over-the-Air Federated Learning with Differential Privacy

Zixi Wang, Arick Grootveld, M. Cenk Gursoy

Research output: Chapter in Book/Entry/PoemConference contribution

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.

Original languageEnglish (US)
Title of host publicationWiseML 2023 - Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning
PublisherAssociation for Computing Machinery, Inc
Pages51-56
Number of pages6
ISBN (Electronic)9798400701337
DOIs
StatePublished - Jun 1 2023
Event5th ACM Workshop on Wireless Security and Machine Learning, WiseML 2023 - Guildford, United Kingdom
Duration: Jun 1 2023 → …

Publication series

NameWiseML 2023 - Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning

Conference

Conference5th ACM Workshop on Wireless Security and Machine Learning, WiseML 2023
Country/TerritoryUnited Kingdom
CityGuildford
Period6/1/23 → …

Keywords

  • differential privacy
  • federated learning
  • hierarchy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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