Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Federated learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchanging only model parameters between clients and servers. This book covers the fundamental concepts of federated learning, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to cover the architectures, algorithms, and system models of federated learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of federated learning through real-world case studies illustrating both centralized and decentralized federated learning. - Presents detailed discussion of the architectures, algorithms, and applications of federated learning- Covers advanced optimization techniques for federated learning algorithms to improve the efficiency and effectiveness of decentralized learning systems- Strikes a balance between the ideas presented, frequently bridging new and engaging material to the fundamental chemistry principle- Shares high-level federated learning security architectures such as FedBoxGuard, which targets single-controller SDN setups by placing "e;white boxes"e; between the data and control planes, and FedLiV, which tackles the non-IID data problem by using heterogeneous models- Presents advanced techniques such as differential privacy, Poisson binomial mechanism vertical federated learning (PBM-VFL), a communication-efficient vertical federated learning algorithm, quantum federated learning, and blockchain-enabled federated learning

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Federated learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchanging only model parameters between clients and servers. This book covers the fundamental concepts of federated learning, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to cover the architectures, algorithms, and system models of federated learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of federated learning through real-world case studies illustrating both centralized and decentralized federated learning. - Presents detailed discussion of the architectures, algorithms, and applications of federated learning- Covers advanced optimization techniques for federated learning algorithms to improve the efficiency and effectiveness of decentralized learning systems- Strikes a balance between the ideas presented, frequently bridging new and engaging material to the fundamental chemistry principle- Shares high-level federated learning security architectures such as FedBoxGuard, which targets single-controller SDN setups by placing "e;white boxes"e; between the data and control planes, and FedLiV, which tackles the non-IID data problem by using heterogeneous models- Presents advanced techniques such as differential privacy, Poisson binomial mechanism vertical federated learning (PBM-VFL), a communication-efficient vertical federated learning algorithm, quantum federated learning, and blockchain-enabled federated learning

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Federated learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchanging only model parameters between clients and servers. This book covers the fundamental concepts of federated learning, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to cover the architectures, algorithms, and system models of federated learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of federated learning through real-world case studies illustrating both centralized and decentralized federated learning. - Presents detailed discussion of the architectures, algorithms, and applications of federated learning- Covers advanced optimization techniques for federated learning algorithms to improve the efficiency and effectiveness of decentralized learning systems- Strikes a balance between the ideas presented, frequently bridging new and engaging material to the fundamental chemistry principle- Shares high-level federated learning security architectures such as FedBoxGuard, which targets single-controller SDN setups by placing "e;white boxes"e; between the data and control planes, and FedLiV, which tackles the non-IID data problem by using heterogeneous models- Presents advanced techniques such as differential privacy, Poisson binomial mechanism vertical federated learning (PBM-VFL), a communication-efficient vertical federated learning algorithm, quantum federated learning, and blockchain-enabled federated learning