Federated Learning for Wireless Networks by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu HanEnglish | EPUB | 2021 | 265 Pages | ISBN : 9811649626 | 28.8 MB
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.
