Work

Data-Driven and Privacy-Preserving Cooperation in Decentralized Learning

Year: 2024

Type: conference-paper

Abstract: Decentralized learning scenarios offer the opportunity of a flexible cooperation between learning nodes; in other words, each node may cooperate with an arbitrary subset of its peers. In such scenarios, we tackle the problem of choosing the nodes that cooperate towards the training of a machine learning model, hence, tweaking the cooperation graph connecting the nodes themselves. We propose and evaluate a data-driven approach to the problem, by proposing three metrics to choose the edges to activate in the cooperation graph, and an efficient iterative algorithm exploiting them. Through our performance evaluation, which leverages state-of-the-art datasets and neural network architectures, we find that privacy-preserving metrics accounting for the difference between local datasets are very effective in identifying the best edges to activate to improve the efficiency of model training without hurting performance.

Source: Conference on Local Computer Networks

DOI: 10.1109/lcn60385.2024.10639653

Authors: Francesco Malandrino, Carlos Barroso Fernandez, Carlos J. Bernardos Cano, Carla Fabiana Chiasserini, Antonio De La Oliva, Mahyar Onsori