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Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices

arXiv 2509.12814 ↗First posted 16 Sep 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy,unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes uplink transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments. Index Terms: Federated learning, IoT, finite blocklength, quantization, energy efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 16 Sep 2025.

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References · 8

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  3. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  5. 2015Quantized Convolutional Neural Networks for Mobile Devicescited by 215
  6. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  7. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
  8. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10

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  3. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
  4. 2025Lightweight Federated Learning over Wireless Edge Networks
  5. 2021Quantized Federated Learning under Transmission Delay and Outage Constraints
  6. 2024Federated Learning With Energy Harvesting Devices: An MDP Framework
  7. 2024BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
  8. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design