Federated Learning for Privacy-Preserving AI

Federated Learning (FL) is an emerging machine learning paradigm that enables decentralized training of models while ensuring data pri-vacy.Unlike traditional machine learning approaches that require data centralization, FL allows multiple clients to collaboratively train a global model without sharing raw data.This paper explores the role of FL in privacy-preserving AI, discussing its advantages, key privacy-enhancing techniques, applications, challenges, and future directions.