Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance

Raúl Llasag Rosero (), Catarina Silva (), Bernardete Ribeiro () and Bruno F. Santos ()
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Raúl Llasag Rosero: CISUC - Centre for Informatics and Systems of the University of Coimbra
Catarina Silva: CISUC - Centre for Informatics and Systems of the University of Coimbra
Bernardete Ribeiro: CISUC - Centre for Informatics and Systems of the University of Coimbra
Bruno F. Santos: Delft University of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 21, 4015-4034

Abstract: Abstract Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information. However, conventional FL methods struggle to handle situations where data samples have diverse features and sizes. We propose a Hybrid Federated Learning solution with label synchronization to overcome this challenge. Our FedLabSync algorithm trains a feed-forward Artificial Neural Network while alerts that it can aggregate knowledge of other ML architectures compatible with the Stochastic Gradient Descent algorithm by conducting a penalized collaborative optimization. We conducted two industrial case studies: product inspection in Bosch factories and aircraft component Remaining Useful Life predictions. Our experiments on decentralized data scenarios demonstrate that FedLabSync can produce a global AI model that achieves results on par with those of centralized learning methods.

Keywords: Federated Learning; Machine Learning; Hybrid Federated Learning; Artificial Neural Network; Stochastic Gradient Descent (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02298-8

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