This comprehensive guide demystifies federated learning, a technique that allows machine learning models to be trained across multiple decentralized devices or servers while keeping the data local. By focusing on privacy and security, federated learning enables organizations to leverage the vast amounts of data available without compromising individual privacy. Federated Learning: Privacy-Preserving Machine Learning in the Decentralized Age is an essential read for anyone interested in the intersection of privacy, machine learning, and decentralized systems. It provides a thorough understanding of how federated learning works and its potential to reshape the future of data privacy and AI.
