Invited Talk
in
Workshop: Cross-Community Federated Learning: Algorithms, Systems and Co-designs

Federated Learning for EdgeAI: New Ideas and Opportunities for Progress

Radu Marculescu

2022 Invited Talk
in
Workshop: Cross-Community Federated Learning: Algorithms, Systems and Co-designs

Abstract

EdgeAI aims at the widespread deployment of AI on edge devices. To this end, a critical requirement of future ML systems is to enable on-device automated training and inference in distributed settings, wherever and whenever data, devices, or users are present, without sending the training (possibly sensitive) data to the cloud or incurring long response times. Starting from these overarching considerations, we consider on-device distributed learning, the hardware it runs on, and their co-design to allow for efficient federated learning and resource-aware deployment on edge devices. We hope to convey the excitement of working in this problem space that brings together topics in ML, optimization, communications, and application-hardware (co-)design.

Speaker

Radu Marculescu

Radu Marculescu is the Laura Jennings Turner Chair in Engineering and Professor in the Electrical and Computer Engineering department at The University of Texas at Austin. He received his Ph.D. in Electrical Engineering from the University of Southern California in 1998. Radu’s current research focuses on machine learning methods and tools for modeling and optimization of EdgeAI, embedded systems, cyber-physical systems, and social networks.

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