… and interference), limited wireless resources (eg, transmit power and radio spectrum), and
… study of how distributed learning can be efficiently and effectively deployed over wireless …

… wireless channels. In a nutshell, the above-mentioned wireless DML frameworks can
enable a distributed learning … available at the mobile devices or clients. The frameworks and …

… communication-efficient and distributed learning frameworks built upon jointly optimizing the
types of communication … In mobile communication systems, uplink data rates are often much …

… We highlight the advantages of hybrid distributed learning architectures compared to state-of…
to improve wireless communication performance. Finally, we identify the existing challenges …

… , wireless communication security and privacy issues have been ignored to some extent.
Since data security and privacy issues … an FL-based distributed learning architecture in 6G. In …

… due to its objective, which is parallelizing the gradient computation and aggregation across
multiple worker nodes, to distinguish this type of learning from the distributed learning that …

… Motivated by the above, we will study distributed learning architecture to train ML models
for supporting advanced applications, like fire tracking and flood monitoring, in wireless UAV …

… a comprehensive study of how distributed learning can be efficiently and effectively
deployed in wireless networks. In particular, we introduce four distributed learning frameworks, …

… intelligent D2D data offloading can provide to distributed learning, finding in particular that
up to … are possible compared to conventional federated learning. Our results reveal that these …

… of distributed learning while considering the dynamics and uncertainty of wireless connections
… for different learning tasks without explicit information about the wireless environment and …