Federated Learning for Privacy-Preserving Machine Learning: Decentralized Model Training with Enhanced Data Security

Abstract

Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects, algorithms, and decentralized architecture. By keeping raw data localized, Federated Learning enables global models while safeguarding individual privacy, fostering collaboration across sectors like healthcare, finance, and IoT. It also addresses challenges such as privacy vulnerabilities and model aggregation across devices, proposing solutions to strengthen Federated Learning's effectiveness. Ultimately, this study highlights Federated Learning's pivotal role in the future of AI, where privacy preservation and collaboration are key. By balancing model performance with data privacy, Federated Learning stands as a promising framework for responsible and inclusive AI development.