ESANN 2024
Another good news this week! Our paper, “Federated Learning in a Semi-Supervised Environment for Earth Observation Data” has been accepted at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2024), which will be held in Bruges (Belgium) from October 9 to October 11.
In this paper, we propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor of a classification model via weight averaging. The fully connected layers of the supervised models are also averaged in a federated fashion. We show the effectiveness of our approach by comparing it with the state-of-the-art federated algorithm, an isolated and a centralised baseline, on novel cloud detection datasets (just accepted at ICIP 2024).
Arxiv preprint will be public as soon as possible.
GitHub code repository: https://github.com/CasellaJr/FedRec
Authors: Bruno Casella *, Alessio Barbaro Chisari *, Marco Aldinucci, Sebastiano Battiato, Mario Valerio Giuffrida
*Both authors contributed equally to this work