Federated Learning in a Semi-Supervised Environment for Earth Observation Data
Contributo in Atti di convegno
Data di Pubblicazione:
2024
Abstract:
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. Our code is available at https://github.com/CasellaJr/FedRec.
Tipologia CRIS:
04A-Conference paper in volume
Elenco autori:
Casella, Bruno; Chisari, Alessio Barbaro; Aldinucci, Marco; Battiato, Sebastiano; Giuffrida, Mario Valerio
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Link al Full Text:
Titolo del libro:
ESANN 2024 Proceedings - 32th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning