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Data analysis algorithm for internet of things based on federated learning with optical technology

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Abstract

As the Internet of Things (IoT) progresses, federated learning (FL), a decentralized machine learning framework that preserves every participant's data privacy, has grown in prominence. However the IoT data possessed by corporations and enterprises frequently has different distributed properties (Non-IID), which has a negative influence on their federated learning. Throughout the local training stage, this issue makes client forget about global information, which therefore slows convergence in general and reduce accuracy. The suggested technique called FedARD, which depends on relationship-based insight distillation, to improve the mining of higher grade global knowledge through local algorithms from a superior dimensions viewpoint over their term of local training in order to maintain global knowledge and prevent forgetting. In order for students to further efficiently acquire global knowledge, it also established an entropy-wise adaptive weights module to control the proportional of loss in single sample knowledge distillation against relational knowledge distillation. FedARD performed stronger than other sophisticated FL approaches in terms of convergence speed along with classification accuracy, as determined by a set of studies on CIFAR10 as well as CIFAR100.

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Authors and Affiliations

  1. Centre for Artificial Intelligence, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

    Vibha Tiwari

  2. School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, Madhya Pradesh, 466114, India

    S. Ananthakumaran

  3. Department of Computer Science and Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India

    M. Rajani Shree

  4. Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India

    M. Thangamani

  5. Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

    M. Pushpavalli

  6. Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Swati Bula Patil

Authors
  1. Vibha Tiwari
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  2. S. Ananthakumaran
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  3. M. Rajani Shree
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  4. M. Thangamani
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  5. M. Pushpavalli
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  6. Swati Bula Patil
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Contributions

VT; AS-Conceptualization; Methodology; Software; Formal analysis; Writing-Original Draft. RSM; TM-Investigation; Supervision. PM; SBP-Writing-Review & Editing; Supervision; Project administration.

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Correspondence to Vibha Tiwari.

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Tiwari, V., Ananthakumaran, S., Shree, M.R. et al. Data analysis algorithm for internet of things based on federated learning with optical technology. Opt Quant Electron 56, 572 (2024). https://doi.org/10.1007/s11082-023-05972-6

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