Amir Leshem and Kobi Cohen
Bar Ilan University, Ben-Gurion University of the Negev

Recent years have shown significant advances in many signal processing tasks based on machine learning techniques. Deep learning as well as reinforcement learning techniques have shown a tremendous value for classification, noise reduction and many other tasks. Recent advances in transferring the learning process to the edge of the network in order to protect the privacy of users’ data, as well as exploit the computational resources available at the mobile devices stimulated the development of techniques such as federated learning. In contrast, learning over networks of selfish agents is much less understood and holds the potential for the next leap in learning techniques. To allow distributed learning, both in the federated and distributed contexts, efficient communication techniques are required to save both energy and bandwidth. The tutorial will present recent results related to distributed learning under communication constraints. We will survey basic protocols which can be utilized to achieve efficient learning and then implement them to multiple examples of collaborative spectrum access as well as other resource sharing problems.

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