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Under the federated learning paradigm, a set of nodes can cooperatively traina machine learning model with the help of a centralized server. Such a serveris also tasked with assigning a weight to the information received from eachnode, and often also to drop too-slow nodes from the learning process. Bothdecisions have major impact on the resulting learning performance, and caninterfere with each other in counterintuitive ways. In this paper, we focus onedge networking scenarios and investigate existing and novel approaches to suchmodel-weighting and node-dropping decisions. Leveraging a set of real-worldexperiments, we find that popular, straightforward decision-making approachesmay yield poor performance, and that considering the quality of data inaddition to its quantity can substantially improve learning.

Federated Learning at the Network Edge: When Not All Nodes are Created Equal