Choose Before You Label: Efficient Node Selection in Constrained Federated Learning

Paper i proceeding, 2025

In many cases, federated learning (FL) has to take place in communication constrained scenarios, where we must select a small number of learning nodes to reduce bandwidth consumption. Furthermore, such nodes may also have computational constraints, i.e., they can store small datasets and process and perform as little data processing as possible. In this context, it is of paramount importance to make node selection decisions before the learning process begins, and without labeling information. We tackle this daunting task through a two-pronged approach, where we (i) introduce a new metric called loneliness, defined on unlabeled datasets, and (ii) propose a novel algorithm called Goldilocks to make node selection decisions and identify the data to be labeled. Through both a theoretical and an experimental analysis, we show that loneliness is strongly linked with learning performance (i.e., test accuracy). Furthermore, our performance evaluation, including three state-of-the-art datasets and a comparison against centralized learning, demonstrates that Goldilocks outperforms approaches based upon a balanced label distribution by providing over 70% accuracy improvement, in spite of being efficient to compute and not using labeling information.