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Conference Papers Year : 2023

Federated Representation Learning for Indoor-Outdoor Detection in beyond 5G networks

Abstract

Scarcity of labelled datasets makes it challenging to train robust mobile user environment detection models. Labelling and centralizing large data amounts for training is expensive. To address these issues, semi-supervised learning techniques aim to reduce data labelling, while Federated Learning (FL) avoids centralizing the data. In this work, we propose a novel approach for Indoor/Outdoor detection by combining the strengths of federated and semi-supervised learning. It consists of 2 steps: (1) Unsupervised Federated representation Learning to learn representations using large unlabelled data. We leverage unlabelled data from diverse sources situated across various geographical locations. Through FL, we develop high-quality representations by jointly learning from this distributed unlabelled data. (2) We then capitalize on the acquired representations and further employ transfer learning to achieve accurate detection using a reduced amount of labelled data. We also add an optimization module referred as User Behavioural Optimizer that corrects environment detection errors by tracking behavioural anomalies. We obtain an F1-score of 95.06% using only 30% of the entire amount of labelled data available.
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emse-04316332 , version 1 (30-11-2023)

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  • HAL Id : emse-04316332 , version 1

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Sid Ali Hamideche, Marie Line Alberi Morel, Kamal Singh, César Viho. Federated Representation Learning for Indoor-Outdoor Detection in beyond 5G networks. GLOBECOM 2023 - IEEE Global Communications Conference, Dec 2023, Kuala Lampur, Malaysia. pp.1-6. ⟨emse-04316332⟩
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