Federated Learning in Data Science – Collaborative Model Training While Preserving Data Privacy

Abstract

Data-driven models in modern data science often require large-scale datasets that may be distributed across multiple organizations or devices. Traditional centralized training approaches face challenges related to data privacy, security, and regulatory compliance. Federated learning (FL) provides a decentralized solution, enabling collaborative model training across multiple devices or institutions while keeping raw data local. This paper explores the architecture, methodologies, and applications of federated learning in data science. Comparative analysis illustrates performance, communication efficiency, and privacy preservation, along with practical applications in healthcare, finance, and IoT environments. Edge cases and future research directions are also discussed.

KEYWORDS: Federated Learning, Data Privacy, Collaborative Machine Learning, Decentralized AI, Secure Model Training, Edge Computing