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Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design

arXiv 2508.01745 ↗First posted 3 Aug 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. local data. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy-convergence model capturing the coupled impact of these components, and develop a Bayesian optimization(BO)-based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. To the best of our knowledge, this is the first work to jointly optimize FL performance from the perspectives of data, computation, and communication under unreliable wireless conditions. Experiments on representative CV tasks show that FedDPQ achieves superior convergence speed and energy efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 3 Aug 2025.

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References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2018Sparsified SGD with Memorycited by 376
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  8. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  9. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  10. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  11. 2023Denoising Diffusion Step-aware Modelscited by 24
  12. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
6 more
  1. 2023Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspectivecited by 17
  2. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  3. 2025Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networkscited by 8
  4. 2022Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaversecited by 7
  5. 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
  6. 2024FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncodercited by 3

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  2. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
  3. 2026CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge
  4. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
  5. 2024On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks
  6. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks
  7. 2025Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing
  8. 2024Federated Learning With Energy Harvesting Devices: An MDP Framework