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.
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