FEDERATED LEARNING IN ARTIFICIAL INTELLIGENCE: A PRIVACY-PRESERVING APPROACH FOR DISTRIBUTED MACHINE LEARNING SYSTEMS - Vasava, Mehul J; Gamit, Vrunda; Panchal, Adesh V; Patel, Mihir M; Gohil, Hemangini (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The advent of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized numerous industries by enabling systems to learn from data and make intelligent decisions. However, as the demand for data-driven models grows, so does the concern for data privacy and security. Traditional centralized learning paradigms collect data on a single server, creating substantial privacy risks, legal implications, and system inefficiencies. Federated Learning (FL) has emerged as a novel paradigm that enables model training across decentralized data sources without transferring raw data to a central server. This approach significantly enhances privacy preservation, reduces latency, and complies with data protection regulations such as GDPR and HIPAA. The paper delves into the core principles of FL, its system architectures, communication protocols, and privacy-preserving techniques such as differential privacy and homomorphic encryption. We also explore key applications across healthcare, finance, and IoT, highlighting both the opportunities and challenges in real-world implementation. Comparative analyses are presented between FL and traditional machine learning in terms of performance, privacy, and scalability. Finally, the paper addresses open research challenges and potential future directions to make federated learning more robust, scalable, and universally deployable.