A Comprehensive Study of Federated Learning Schemes for the Artificial Intelligence of Things

Abstract

Massive amounts of data are produced continuously by billions of Internet of Things (IoT) devices and analyzed via Machine Learning (ML) models to serve a wide variety of needs. However, the high communication cost of traditional ML approaches coupled with data privacy issues makes them unpractical for many IoT applications, especially in healthcare where medical records contain sensitive information that can compromise patient privacy. As a result, current research has begun to investigate Federated Learning (FL) as a new paradigm addressing these critical concerns by training ML models without sharing private data. This paper provides a comprehensive study of existing FL algorithms and discusses their applicability in a medical IoT context using publicly available datasets. This is proceeded by integrating such schemes in the FedML framework using real-world medical datasets in both simulated and on-device federated settings to investigate the impact of clients number, communication loss, and data compression on model performance, energy, time, and code footprint. The paper also makes suggestions on open research issues that need to be addressed by the community.