A privacy preserving framework for federated learning in smart healthcare systems

Wenshuo Wang, Xu Li, Xiuqin Qiu, Xiang Zhang, Jindong Zhao, Vladimir Brusic

Research output: Journal PublicationArticlepeer-review

15 Citations (Scopus)


Federated Learning (FL) is a platform for smart healthcare systems that use wearables and other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer the connection between physiological data in training datasets with FL clients and reveal the identities of participants to the attackers. We propose a comprehensive smart healthcare framework for sharing physiological data, named FRESH, that is based on FL and ring signature defense from the attacks. In FRESH, physiological data are collected from individuals by wearable devices. These data are processed by edge computing devices (e.g., mobile phones, tablet PCs) that train ML models using local data. The model parameters are uploaded by edge computing devices to the central server for joint training of FL models of disease prediction. In this procedure, certificateless ring signature is used to hide the source of parameter updates during joint training for FL to effectively resist SIAs. In the proposed ring signature schema, an improved batch verification algorithm is designed to leverage additivity of linear operations on elliptic curves and to help reduce the computing workload of the server. Experimental results demonstrate that FRESH effectively reduces the success rate of SIAs and the batch verification method significantly improves the efficiency of signature verification. FRESH can be applied to large scale smart healthcare systems with FL involving large numbers of users.

Original languageEnglish
Article number103167
JournalInformation Processing and Management
Issue number1
Publication statusPublished - Jan 2023


  • Federated learning
  • Privacy preserving
  • Ring signature
  • Smart healthcare system
  • Source inference attack

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences


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