Federated Learning and Privacy-Preserving AI in Healthcare

Abstract

The healthcare industry collects large volumes of sensitive information every day; however, it shares these data only to a limited extent because of regulatory requirements, competitive concerns, and other constraints. This makes it difficult to develop AI models for cybersecurity applications. The proposed solution is federated learning (FL), a method that enables medical centers to train models independently without sharing their data with one another. Only model updates are exchanged among participating organizations, preserving the privacy of patient data. All model updates are then assembled by a central database to produce a more accurate global model. This chapter discusses training under both IID and non-IID data distributions. Besides, it also covers the effectiveness of the most prominent FL healthcare processes, such as FedAvg, FedProx, SCAFFOLD, and Per-FedAvg. The chapter also analyzes methods of privacy, including homomorphic encryption, secure aggregation, and differential privacy. Potential risks, including data leakage due to new models, and their ways of prevention are also discussed. The chapter also addresses practical considerations, including regulatory requirements (e.g., HIPAA and GDPR), FL deployment across hospitals and devices, and communication cost reduction. Lastly, some practical applications, including the detection of abnormalities in healthcare data, secure communication among hospitals, medical image analysis, and the safety of wearable devices, have also been highlighted. The chapter also provides suggestions for future research and their practical implementation.