Abstract
The amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. Processing and analyzing raw data at a cloud server faces several challenges such as bandwidth, congestion, latency, privacy and security. Fog computing brings computational resources closer to IoT that addresses some of these issues. These IoT devices have low computational capability, which is insufficient to train machine learning. Mining hidden patterns and inferential rules from continuously growing data is crucial for various applications. Due to growing privacy concerns, privacy preserving machine learning is another aspect that needs to be inculcated. In this paper, we have proposed a fog enabled distributed training architecture for machine learning tasks using resources constrained devices. The proposed architecture trains machine learning model on rapidly changing data using online learning. The network is inlined with privacy preserving federated learning training. Further, the learning capability of architecture is tested on a real world IIoT use case. We trained a neural network model for human position detection in IIoT setup on rapidly changing data.
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References
Al-Khafajiy, M., Baker, T., Waraich, A., Alfandi, O., Hussien, A.: Enabling high performance fog computing through fog-2-fog coordination model. In: 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) (2019). https://doi.org/10.1109/AICCSA47632.2019.9035353
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. MCC ’12, Association for Computing Machinery, New York, NY, USA (2012). https://doi.org/10.1145/2342509.2342513, https://doi.org/10.1145/2342509.2342513
Buyya, R., Srirama, S.N.: Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons (2019)
Chang, C., Srirama, S.N., Buyya, R.: Internet of Things (IoT) and New Computing Paradigms (2018)
Consortium, O.: OpenFog Reference Architecture for Fog Computing, Technical Report (February 2017)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013). https://doi.org/10.1016/j.future.2013.01.010, https://www.sciencedirect.com/science/article/pii/S0167739X13000241
Hazra, A., Adhikari, M., Amgoth, T., Srirama, S.N.: Joint computation offloading and scheduling optimization of iot applications in fog networks. IEEE Trans. Netw. Sci. Eng. 7(4), 3266–3278 (2020)
Holst, A.: IoT connected devices worldwide 2019–2030, August 2021. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/
Kamath, G., Agnihotri, P., Valero, M., Sarker, K., Song, W.Z.: Pushing analytics to the edge. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2016). https://doi.org/10.1109/GLOCOM.2016.7842181
Konečný, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR abs/1610.02527 (2016). http://arxiv.org/abs/1610.02527
Konečný, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning (2016). https://arxiv.org/abs/1610.05492
Li, Y., Li, H., Xu, G., Xiang, T., Huang, X., Lu, R.: Toward secure and privacy-preserving distributed deep learning in fog-cloud computing. IEEE Internet Things J. 7(12), 11460–11472 (2020). https://doi.org/10.1109/JIOT.2020.3012480
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans. Ind. Inform. 16(3), 2134–2143 (2020). https://doi.org/10.1109/TII.2019.2942179
Luo, S., Chen, X., Wu, Q., Zhou, Z., Yu, S.: HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Trans. Wireless Commun. 19(10), 6535–6548 (2020). https://doi.org/10.1109/TWC.2020.3003744
McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 1273–1282. PMLR (20–22 Apr 2017). https://proceedings.mlr.press/v54/mcmahan17a.html
Munusamy, A., et al.: Edge-centric secure service provisioning in IoT-Enabled maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, pp. 1–10 (2021). https://doi.org/10.1109/TITS.2021.3102957
Saha, R., Misra, S., Deb, P.K.: FogFL: fog-assisted federated learning for resource-constrained IoT devices. IEEE Internet Things J. 8(10), 8456–8463 (2021). https://doi.org/10.1109/JIOT.2020.3046509
Savazzi, S.: Federated learning: example dataset (fmcw 122ghz radars) (2019). https://doi.org/10.21227/8yqc-1j15
Savazzi, S., Nicoli, M., Rampa, V.: Federated learning with cooperating devices: a consensus approach for massive IoT networks. IEEE Internet Things J. 7(5), 4641–4654 (2020). https://doi.org/10.1109/JIOT.2020.2964162
Srirama, S.N., Dick, F.M.S., Adhikari, M.: Akka framework based on the actor model for executing distributed fog computing applications. Future Gener. Comput. Syst. 117, 439–452 (2021)
Acknowledgment
We acknowledge financial support to UoH-IoE by MHRD, India (F11/9/2019-U3(A)).
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Kumar, A., Srirama, S.N. (2021). Fog Enabled Distributed Training Architecture for Federated Learning. In: Srirama, S.N., Lin, J.CW., Bhatnagar, R., Agarwal, S., Reddy, P.K. (eds) Big Data Analytics. BDA 2021. Lecture Notes in Computer Science(), vol 13147. Springer, Cham. https://doi.org/10.1007/978-3-030-93620-4_7
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