Federated Learning: A New Paradigm for Decentralized and Privacy-Preserving AI

Abstract

Federated Learning represents a revolutionary approach in artificial intelligence, emphasizing decentralized and privacy-preserving methodologies. Unlike traditional machine learning, where data is centralized on a single server, federated learning enables multiple devices or nodes to collaboratively train a shared model while keeping their data locally. This approach addresses critical concerns about data privacy and security by ensuring that raw data never leaves its original location. Instead, only model updates are communicated between devices and a central server, reducing the risk of sensitive information exposure. As a result, federated learning supports the development of AI systems that can operate efficiently and effectively in diverse and distributed environments, offering a significant advancement in both privacy protection and collaborative learning.