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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Hardware-Aware Federated Learning for Resource-Constrained IoT Devices |
| Authors Name: | Abhinav Garg , Sujal Jain , Nikhil Sharma |
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IJRTI_210630
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| Published Paper Id: | IJRTI2603103 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Edge IoT devices are increasingly targeted for on- device intelligence using federated learning (FL). However, conventional FL imposes heavy communication and energy costs that make it impractical for battery-constrained, bandwidth-limited deployments with heterogeneous (non- IID) data. In this paper we present a practical, communication-efficient FL framework that combines update quantization with top-k sparsification and evaluates its performance under realistic edge conditions. We implement the framework in MATLAB and conduct an extensive empirical study on EMNIST and a synthetic IoT sensor dataset across varied Dirichlet non-IID severities, client dropout rates, and multiple random seeds. Our experiments show that combining low-bit quantization (4 bits) with sparsification (top 2–5%) yields orders-of- magnitude reductions in transmitted bytes and energy while maintaining high model fidelity: e.g., compared to uncompressed FedAvg (baseline accuracy 84.90%) a 4-bit + sparsity configuration reduces communication by up to ~97.7% and energy consumption by a similar factor, while achieving ≈79.1% accuracy. We provide detailed convergence analysis, statistical confidence intervals across seeds, and an energy model translating bytes → Joules to quantify device-level savings. Finally, we analyze failure modes and robustness under extreme heterogeneity and client dropout, and provide reproducible MATLAB code and result artifacts. Our findings show that careful joint compression enables practical FL deployments on edge IoT devices without sacrificing scientifically meaningful accuracy. |
| Keywords: | Federated Learning, Edge IoT, Communication Compression, Quantization, Sparsification, Non-IID Data |
| Cite Article: | "Hardware-Aware Federated Learning for Resource-Constrained IoT Devices", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b14-b22, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603103.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2603103
Registration ID:210630
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b14-b22 Country: South West, New delhi , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603103 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603103 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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