… : network, heterogeneity, and proximity. It considers a multi-layer hybrid learning framework
consisting of heterogeneous … of the local networks among the heterogeneous nodes at each …

… architectures and their applications to wireless networks. The key features of different DML
architectures … An illustration of a hybrid/hierarchical distributed machine learning framework, …

… This is done via the use of multihop routes, which incorporate wireless networks and … based
on multimodal hybrid deep learning models for dealing with medically heterogeneous health-…

… as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid
… We highlight the advantages of hybrid distributed learning architectures compared to state-of…

… distributed learning over wireless networks. The main focus of this article is the widely-studied
FL framework for distributed learning… in modern deep learning architectures. For example, …

… users (UEs), could exploit machine learning (ML) algorithms to … distributed learning algorithms
in wireless networks, not all … to unreliable and randomly fading wireless channels from the …

… Mobile networks have already created a large data source to efficiently manage HetNets by
… -based load balancing scheme was proposed for hybrid WiFi/LiFi networks. RL is trained to …

… and model inertia in distributed machine learning. We then propose networkaware dynamic
… Remark 1: In general, the optimal data offloading strategy may be a hybrid of the two …

… issues into the context of mobile networks, where radio resources … network functionality
operations and distributed machine … distributed machine learning solutions for mobile networks. …

… machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless
network … There are also hybrid MARL systems where both cooperative and competitive …