Federated learning for secure and private data analysis in decentralized networks

Abstract

Federated learning (FL), which allows collaborative machine learning without requiring the centralisation of sensitive data, has become a game-changing concept for private and safe data analysis in decentralised networks. FL enables edge devices or local nodes, such as smartphones, IoT devices, or healthcare facilities, to learn shared models remotely and send only model changes to a central server, in contrast to traditional methods that call for raw data aggregation. This framework lowers communication overhead, mitigates regulatory problems, and greatly improves data privacy and security. FL provides a workable and scalable way to create superior machine learning models in decentralised networks, where data is naturally dispersed and frequently subject to stringent privacy laws. Nevertheless, there are still issues to be resolved, such as managing non-IID data, making sure that systems are resilient to hostile attacks, and preserving effective communication. To further improve FL's privacy-preserving capabilities, recent developments like homomorphic encryption, safe multiparty computation, and differential privacy are being incorporated. This study examines the fundamentals of federated learning, goes over important methods for improving its security and privacy, and talks about how it may be used in a variety of industries, including as healthcare, finance, and smart cities. FL is one of the most important steps to safe and ethical AI in decentralized environments because it allows the collaboration of intelligence and preserves data ownership.