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From Federated to Fog Learning: Expanding Model Training over Wireless Networks

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Updated to CAIT building with larger Auditorium to accommodate bigger attendee audience size. CAIT building on 100 Brett Road, next to Electrical And Computer Engineering Building. All share the same parking lot. Please look out for signage posted with directions to CAIT Auditorium.

From Federated to Fog Learning: Expanding the Frontier of Model Training over Contemporary Wireless Network Systems

Abstract
Fog learning is an emerging paradigm for optimizing the orchestration of artificial intelligence services over contemporary network systems. Different from existing distributed techniques such as federated learning, fog learning emphasizes intrinsically in its design the unique node, network, and data properties encountered in today’s fog networks that span computing elements from the edge to the cloud. An important thread of research in fog learning has been on understanding the role that local topologies formed on an ad-hoc basis among proximal groups of heterogeneous computing elements can play in elevating the achievable tradeoff between intelligence quality and resource efficiency. In this talk, I will discuss recent results on the analysis of fog learning processes which give insights into the impact that these topologies, along with other properties such as model characteristics and fog decision parameters, have on global training performance. Additionally, I will discuss the development of adaptive control methodologies that leverage such relationships for jointly optimizing relevant fog learning metrics.

Distinguished Lecturer Series: https://www.comsoc.org/membership/distinguished-lecturers
Speaker: https://www.comsoc.org/christopher-greg-brinton