Chapter 19: Mobile computing framework for federated learning
Xiang Chena; Fuxun Yub; Zirui Xuc aGeorge Mason University, ECE, Fairfax, VA, United StatesbMicrosoft, Seattle, WA, United StatescCVS Health, Richmond, VA, United States
Abstract
Driven by enormous mobile AI applications like intelligent conversational AI, automatic scene understanding, AI-enhanced photo stylizing and enhancement, and face and fingerprint ID, mobile AI markets have surged exponentially in the last few decades. Traditionally, such mobile AI training tasks highly rely on centralized learning paradigms with all data hosted in a centralized cloud where the training is then being conducted. However, with exploding amounts of data generated from users and end devices, gathering ...
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