Anwesha Mukherjee – författare

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Sections cover fundamental concepts, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to coverage of 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.Federated Learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchange of only model parameters between clients and servers, hence the addition of this new release is ideal for those interested in the topics presented.Includes detailed discussions on the architectures, algorithms, and applications of Federated LearningCovers advanced optimization techniques for Federated Learning algorithms to improve the efficiency and effectiveness of decentralized learning systemsProvides coverage of high-level Federated Learning security architectures such as FedBoxGuard, which targets single-controller SDN setups by placing “white boxes” between the data and control planes, and FedLiV, which tackles the non-IID data problem by using heterogenous modelsIncludes coverage of advanced techniques such as Differential Privacy, Hindmarsh-Rose encryption, and Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees

For low latency and high bandwidth services, edge computing assisted IoT (Internet of Things) has become the pillar for the development of smart environments and their applications such as smart home, smart health, smart traffic management, smart agriculture, and smart city.

For low latency and high bandwidth services, edge computing assisted IoT (Internet of Things) has become the pillar for the development of smart environments and their applications such as smart home, smart health, smart traffic management, smart agriculture, and smart city.

As mobile cloud computing, as well as green computing, will have a major impact on the quality of science and society over the next few years, its knowledge will enrich our readers to be at the forefront of the field. This book reports the latest research advances in the area of green mobile cloud computing.

As mobile cloud computing, as well as green computing, will have a major impact on the quality of science and society over the next few years, its knowledge will enrich our readers to be at the forefront of the field. This book reports the latest research advances in the area of green mobile cloud computing.

This book focuses on resource management in distributed computing systems. The book presents a collection of original, unpublished, and high-quality research works, which report the latest research advances on resource discovery, allocation, scheduling, etc., in cloud, fog, and edge computing. The topics covered in the book are resource management in cloud computing/edge computing/fog computing/dew computing, resource management in Internet of things, resource allocation, scheduling, monitoring, and orchestration in distributed computing systems, resource management in 5G network and beyond, latency-aware resource management, energy-efficient resource management, interoperability and portability, security and privacy in resource management, reliable resource management, trustworthiness in resource management, fault tolerance in resource management, and simulation related to resource management.