Название: Federated Learning Systems: Towards Privacy-Preserving Distributed AIАвтор: Muhammad Habib ur Rehman, Mohamed Medhat Gaber
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2025
Страниц: 179
Язык: английский
Формат: pdf (true), epub
Размер: 19.3 MB
This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value.
Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, Federated Learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.
Google introduced the term Federated Learning (FL) to enable the Machine Learning models to be initially trained at the customers’ or citizens’ devices and systems and later the model updates are aggregated at the centralized cloud servers. Considering this notion of FL, a large plethora of research activities has been performed by researchers and practitioners in academia and industry. Hence, numerous research publications were produced to solve the active research issues in terms of privacy, security, data and model synchronization, model development and deployment, personalization, incentivization, and heterogeneity across the FL systems. This book aims to study the FL ecosystem with a broader perspective to cover the theoretical as well as applied aspects of FL systems.
Contents:
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