Abstract
The proper utilization of distributed network resources solves important issues faced by machine learning algorithms and artificial intelligence in general such as the availability of high-specification processing resources and the availability of datasets. This paper proposes a new Secured Decentralized Distributed Learning Architecture (SDDLA). The new suggested architecture enables distributed learning algorithms to run on distributed datasets without compromising the privacy and security of shared datasets with unauthorized users. Also, the decentralized management approach of distributed entities simplifies the deployment, activation, and utilization of distributed learning. The proposed architecture includes a new data placement and task allocation algorithm that adds a low bandwidth overhead and low processing requirements on the distributed network.
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Almajali, S. (2023). SDDLA: A New Architecture for Secured Decentralized Distributed Learning. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_22
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DOI: https://doi.org/10.1007/978-981-99-0741-0_22
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