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Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13715))

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Abstract

In Internet of Things applications, data generated from devices with different characteristics and located at different positions are embedded into different contexts. This poses major challenges for decentralized machine learning as the data distribution across these devices and locations requires consideration for the invariants that characterize them, e.g., in activity recognition applications, the acceleration recorded by hand device must be corrected by the invariant related to the movement of the hand relative to the body. In this article, we propose a new approach that abstracts the exact context surrounding data generators and improves the reconciliation process for decentralized machine learning. Local learners are trained to decompose the learned representations into (i) universal components shared among devices and locations and (ii) local components that capture the specific context of device and location dependencies. The explicit representation of the relative geometry of devices through the special Euclidean Group SE(3) imposes additional constraints that improve the decomposition process. Comprehensive experimental evaluations are carried out on sensor-based activity recognition datasets featuring multi-location and multi-device data collected in a structured sensing environment. Obtained results show the superiority of the proposed method compared with the advanced solutions.

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Authors and Affiliations

  1. LIPN-UMR CNRS 7030, Université Sorbonne Paris Nord, Paris, France

    Massinissa Hamidi & Aomar Osmani

Authors
  1. Massinissa Hamidi
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  2. Aomar Osmani
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Corresponding author

Correspondence to Aomar Osmani .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Hamidi, M., Osmani, A. (2023). Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13715. Springer, Cham. https://doi.org/10.1007/978-3-031-26409-2_39

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  • DOI: https://doi.org/10.1007/978-3-031-26409-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26408-5

  • Online ISBN: 978-3-031-26409-2

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