Federated Learning-Based Framework for Privacy-Preserving Multi-Institutional Healthcare Data Analytics

Abstract

The fast development of the data on care in various institutions has posed the serious problems associated with data silos and stringent privacy policies, restricting efficient collaborative analytics. Conventional centralized machine learning solutions presuppose the necessity to share data, which raises the chance of privacy loss and exceeds regulatory limitations, including HIPAA and GDPR. To solve these problems, this paper presents a federated learning-based privacy-preserving multi-institutional healthcare data analytics framework. The solution will allow the decentralized training of models in several hospitals at the same time without having to transfer sensitive patient data to the cloud. The framework combines state-of-the-art federated optimization methods, such as FedAvg and FedProx, to support non-uniform data distributions, and differential privacy strategies (DP-SGD) to improve data protection. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are deep learning models that can be used in effective tasks of feature extraction and predicting. Multi-institutional healthcare data under non-identically distributed (non-IID) conditions are used to evaluate the model. It has been shown that through the experimental results the proposed framework is able to attain a higher level of predictive performance with respect to accuracy, precision, recall, F1-score and AUC with a substantial reduction in the chance of data leakage. Moreover, the framework is characterized by better convergence behaviour and communication effectiveness in comparison with the baseline methods. The results demonstrate the promise of federated learning as a scalable, secure, and efficient, next-generation healthcare analytics solution, and it allows collaborative intelligence without jeopardizing patient privacy.