Federated Learning

<p>This book introduces readers to the fundamentals of and recent advances in federated learning focusing on reducing communication costs improving computational efficiency and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory privacy and security requirements. </p>The book starts with a self-contained introduction to artificial neural networks deep learning models supervised learning algorithms evolutionary algorithms and evolutionary learning. Concise information is then presented on multi-party secure computation differential privacy and homomorphic encryption followed by a detailed description of federated learning. In turn the book addresses the latest advances in federate learning research especially from the perspectives of communication efficiency evolutionarylearning and privacy preservation.<p></p>The book is particularly well suited for graduate students academic researchers and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background or as a reference text for graduate courses.       <p></p>