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About the topic:
Many important application domains generate distributed collections of heterogeneous local datasets. These local datasets are related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets. Networked federated learning aims at learning a tailored local model for each local dataset. We formulate networked federated learning using the concept of generalized total variation (GTV) minimization as a regularizer. This formulation unifies and considerably extends recent approaches to federated multi-task learning. We derive precise conditions on the local models as well on their network structure such that our algorithm learns nearly optimal local models. Our analysis reveals an interesting interplay between the (information-) geometry of local models and the (cluster-) geometry of their network.
Alexander Jung received the Ph.D. degree (with sub auspiciis) in 2012 from Technical University Vienna (TU Vienna). After Post-Doctoral periods at TU Vienna and ETH Zurich, he joined Aalto University as an Assistant Professor for Machine Learning in 2015. He leads the group “Machine Learning for Big Data” that studies explainable machine learning in network-structured data.
Dr. Jung first-authored a paper that won a Best Student Paper Award at IEEE ICASSP 2011. He received an AWS Machine Learning Research Award and was the “Computer Science Teacher of the Year” at Aalto University in 2018. Currently, he serves as an associate editor for the IEEE Signal Processing Letters and as the chair of the IEEE Finland Jt. Chapter on Signal Processing and Circuits and Systems. He authored the textbook, Machine Learning: The Basics (Springer, 2022).
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