Abstract
Federated Learning (FL) can enable industrial devices to collaboratively learn a shared Machine Learning (ML) model while keeping all the training data on the device itself. The FL inherently solves the data privacy issues where as it also reduces the cloud data hosting cost and communication overhead. However, existing FL algorithms and frameworks are generically designed and not evaluated against the industrial applications. In case of industrial applications, the data generated from assets and machines are mostly non-independent and Identically Distributed (non-IID) which demands for an investigation and evaluation on the existing FL algorithms against such non-IID industrial data. This paper presents a survey of FL frameworks, algorithms, and optimizers, and evaluates their performance against the industrial data. The paper shares the results of extensive numerical experiments conducted with the edge device, Raspberry pi. The data set used in this study is benchmarking fault classification industrial data, called PRONTO data set, for both IID and non-IID cases. With the experimental results, the paper demonstrates that the adaptive optimization-based FL algorithm, \(\texttt {FedAdam}\) outperforms other algorithms on an industrial time series fault classification application.
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Prathap Kumar, B., Chouksey, S., Amarlingam, M., Ashok, S. (2023). Evaluation of Federated Learning Strategies on Industrial Time Series Fault Classification. In: Chinara, S., Tripathy, A.K., Li, KC., Sahoo, J.P., Mishra, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 660. Springer, Singapore. https://doi.org/10.1007/978-981-99-1203-2_36
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DOI: https://doi.org/10.1007/978-981-99-1203-2_36
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