1 research outputs found
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
Distributed machine learning (DML) techniques, such as federated learning,
partitioned learning, and distributed reinforcement learning, have been
increasingly applied to wireless communications. This is due to improved
capabilities of terminal devices, explosively growing data volume, congestion
in the radio interfaces, and increasing concern of data privacy. The unique
features of wireless systems, such as large scale, geographically dispersed
deployment, user mobility, and massive amount of data, give rise to new
challenges in the design of DML techniques. There is a clear gap in the
existing literature in that the DML techniques are yet to be systematically
reviewed for their applicability to wireless systems. This survey bridges the
gap by providing a contemporary and comprehensive survey of DML techniques with
a focus on wireless networks. Specifically, we review the latest applications
of DML in power control, spectrum management, user association, and edge cloud
computing. The optimality, scalability, convergence rate, computation cost, and
communication overhead of DML are analyzed. We also discuss the potential
adversarial attacks faced by DML applications, and describe state-of-the-art
countermeasures to preserve privacy and security. Last but not least, we point
out a number of key issues yet to be addressed, and collate potentially
interesting and challenging topics for future research