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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Federated Learning for Wireless Networks (Paperback, 1st ed. 2021)
Loot Price: R4,586
Discovery Miles 45 860
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Federated Learning for Wireless Networks (Paperback, 1st ed. 2021)
Series: Wireless Networks
Expected to ship within 10 - 15 working days
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Recently machine learning schemes have attained significant
attention as key enablers for next-generation wireless systems.
Currently, wireless systems are mostly using machine learning
schemes that are based on centralizing the training and inference
processes by migrating the end-devices data to a third party
centralized location. However, these schemes lead to end-devices
privacy leakage. To address these issues, one can use a distributed
machine learning at network edge. In this context, federated
learning (FL) is one of most important distributed learning
algorithm, allowing devices to train a shared machine learning
model while keeping data locally. However, applying FL in wireless
networks and optimizing the performance involves a range of
research topics. For example, in FL, training machine learning
models require communication between wireless devices and edge
servers via wireless links. Therefore, wireless impairments such as
uncertainties among wireless channel states, interference, and
noise significantly affect the performance of FL. On the other
hand, federated-reinforcement learning leverages distributed
computation power and data to solve complex optimization problems
that arise in various use cases, such as interference alignment,
resource management, clustering, and network control.
Traditionally, FL makes the assumption that edge devices will
unconditionally participate in the tasks when invited, which is not
practical in reality due to the cost of model training. As such,
building incentive mechanisms is indispensable for FL networks.
This book provides a comprehensive overview of FL for wireless
networks. It is divided into three main parts: The first part
briefly discusses the fundamentals of FL for wireless networks,
while the second part comprehensively examines the design and
analysis of wireless FL, covering resource optimization, incentive
mechanism, security and privacy. It also presents several solutions
based on optimization theory, graph theory, and game theory to
optimize the performance of federated learning in wireless
networks. Lastly, the third part describes several applications of
FL in wireless networks.
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