Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
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 …