Privacy and security of federated learning in resource-constrained Internet of Things environment: Systematic literature review

Abstract

Federated Learning (FL) has become a suggested method due to the growing adoption of Internet of Things (IoT) devices, tied with the growing need for collaborative learning and data analysis. FL allows for distributed learning, while ensuring the privacy. Nevertheless, the potential for security vulnerabilities and privacy breaches became more and more complex because of the increasing number of devices that are connected. Moreover, FL in Resource-Constrained IoT environments introduces additional challenges due to the nature of these environments, as these environments have devices with limited resources. Additionally, since FL enables collaborative learning across a decentralized IoT environment, ensuring strong security and privacy becomes crucial to protect information and maintain the trust of participants. This research aims to present comprehensive Systematic Literature Review (SLR) of academic articles on security and privacy of Resource-Constrained environments in FL published from 2016 to 2024. The study intends to maximize the knowledge in FL, FL in Resource-Constrained IoT environments, identification of privacy and security concerns, and techniques designed to enhance them in FL.