Federated Learning for Privacy-Preserving Machine Learning

Abstract

Federated Learning (FL) enables multiple devices or organizations to collaboratively train machine learning models without sharing raw data. Instead of centralizing sensitive information, only model updates are exchanged, improving privacy, reducing communication risks, and supporting regulatory compliance. Federated learning is increasingly applied in healthcare, finance, IoT, mobile applications, and smart manufacturing. This paper discusses its principles, applications, challenges, and future directions.