This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data.
Federated Learning Research: Trends and Bibliometric Analysis.- A Review of Privacy-preserving Federated Learning for the Internet-of-Things.- Di erentially Private Federated Learning: Algorithm, Analysis and Optimization.- Advancements of federated learning towards privacy preservation: from federated learning to split learning.- PySyft: A Library for Easy Federated Learning.- Federated Learning Systems for Healthcare: Perspective and Recent Progress.- Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems.- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.
