A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints

Federated learning is a paradigm that proposes protecting data privacy by sharing local models instead of raw data during each iteration of model training. However, these models can be large, with many parameters, provoking a substantial communication cost and having a notable environmental impact.