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IEEE Future Networks Artificial Intelligence & Machine Learning
IEEE Future Networks Artificial Intelligence & Machine Learning
Date: Thursday, April 18, 2024
Time: 12:00 pm to 1:000 pm
Location: Virtual
Learning and Intelligence over Weak Communication Links
Speaker: Prof. Petar Popovski, Aalborg University, Denmark
Besides the fascinating questions on how to train increasingly capable Machine Learning (ML) models and explain their behavior, there is a suite of highly relevant challenges that emerge when ML models become elements of distributed connected systems and networks. A popular instance of this set of problems is federated learning. The first part of the talk will present a federated learning setup over LEO satellite constellation. It will be seen that the predictability of satellite movement can be used to speed up the training process. The second part will deal with a model for supervised learning in which Alice has access to abundant data features but does not have the labels, while Bob is able to provide a correct label for any data point. Alice is connected to Bob through a low-rate communication link and the talk will present strategies that combine active learning and data compression that enable Alice to get the labels. Finally, the third part of the talk discusses generative network layer of communication protocols. This is implemented in an intermediate network node that contains a Generative AI module. When the link to the source is weak, instead of waiting for packets to be routed, the node can generate the packets that need to be sent to the destination. Generative network layer is an early step towards the potential changes in communication protocols based on increasingly capable AI.
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