Resource constrained learning over wireless networks

It is anticipated that the next generation of wireless networks will incorporate artificial intelligence (AI) to a significant degree at all network layers. A major part of this trend is the migration of AI and machine learning functions to the network edge. There are several reasons for this: (i) a growing number of AI applications demand implementations involving end-user devices, (ii) much data of interest are collected at the network edge, and (iii) fog/edge computing has emerged to take advantage of the increasing sophistication of end-user devices. A notable framework for engaging the wireless network edge in machine learning is wireless federated learning, in which multiple end-user devices collaborate with the help of an aggregator to build a common model, each using its local data. In this framework, exchanges between end-user devices and the aggregator necessarily take place over wireless links. Since wireless networks are notoriously resource-limited, this creates a situation in which the interactions between the wireless medium and machine learning algorithms must be considered as a factor in the design and implementation of AI applications. This paper explores aspects of this problem, including trade-offs among energy consumption and other criteria such as bandwidth efficiency, learning rate and data privacy. This article is part of the discussion meeting issue 'Bits, neurons and qubits for sustainable AI'.