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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References
Almajali, S., Abou-Tair, D.E.D.I.: Cloud based intelligent extensible shared context services. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC). IEEE (2017)
Almajali, S., Abou-Tair, D., Salameh, H.B., Ayyash, M., Elgala, H.: A distributed multi-layer MEC-cloud architecture for processing large scale IoT-based multimedia applications. Multimed. Tools Appl. 78(17), 24617–24638 (2018). https://doi.org/10.1007/s11042-018-7049-3
Gao, Y., et al.: End-to-end evaluation of federated learning and split learning for internet of things. In: 2020 International Symposium on Reliable Distributed Systems (SRDS). IEEE (2020)
Hamdan, S., Almajali, S., Ayyash, M.: Comparison study between conventional machine learning and distributed multi-task learning models. In: 2020 21st International Arab Conference on Information Technology (ACIT). IEEE (2020)
Hamdan, S., Almajali, S., Ayyash, M., Salameh, H.B., Jararweh, Y.: An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems. Simul. Model. Pract. Theory 122, 102685 (2023)
Hamdan, S., Ayyash, M., Almajali, S.: Edge-computing architectures for internet of things applications: a survey. Sensors 20(22), 6441 (2020)
Kubat, M.: An Introduction to Machine Learning. Springer, Cham (2017)
Lu, T., Ai, Q., Lee, W.J., Wang, Z., He, H.: An aggregated decision tree-based learner for renewable integration prediction. In: 2018 IEEE Industry Applications Society Annual Meeting (IAS). IEEE (2018)
Zenko, B., Todorovski, L., Dzeroski, S.: A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods. In: Proceedings 2001 IEEE International Conference on Data Mining. IEEE (2001)
Zhang, Z., Yin, L., Peng, Y., Li, D.: A quick survey on large scale distributed deep learning systems. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE (2018)
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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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