Federated Learning for Privacy Preserving AI: A Scalable Approach to Decentralized Model Training in Healthcare and Finance

Abstract

The increase in demand of data driven decision making in sensitive fields like healthcare and finance requires machine learning frameworks that maintain strict data privacy and follow regulations. Federated Learning (FL) provides a decentralized way to train models. It allows multiple organizations to learn together from distributed datasets without sharing raw data. But, traditional FL methods, such as Federated Averaging (FedAvg), face issues in real world situations. These issues arise from different data distributions among clients and the risk of information leaks from shared model updates. In this research study, we introduce a new federated learning framework with two main innovations: First the adaptive aggregation strategy that adjusts client contributions based on how stable they are and their quality, and second an optional differential privacy module at the server to make sure privacy guarantees. We tested the framework on two publicly available datasets: a heart disease dataset from the University of California, Irvine (UCI) repository and a large financial dataset from Kaggle. This simulates collaboration between hospitals and financial institutions. Experimental results show that our adaptive aggregation method boosts model accuracy by up to 4.2% compared to FedAvg, while still performing well even with differential privacy applied. The model achieves an AUC of 0.93 and an F1 score of 0.891, with minimal communication overhead. These results confirm the framework’s strength and its ability to support the ethical use of Artificial Intelligence in regulated and data sensitive areas. They also recommend it can scale effectively across larger federated networks.

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