SPAWC 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, Special Session on Distributed Learning and Control for Future Wireless Networks, 26-29 May 2020, Atlanta, GA, USA (Virtual Conference)
We consider a distributed learning system, where a parameter server (PS) assigns data and computational tasks to edge devices to build a global model. Distributing data to multiple workers involves communication between PS and edge devices and entails a fundamental tradeoff between computation and communication. In this paper, we aim at characterizing
the optimal number of edge devices required for guaranteeing convergence and for achieving a certain accuracy within a finite time horizon.
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