QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
arXiv:2603.17507v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are increasingly available on edge devices, their potential to reduce the energy overhead of fine-tuning remains underexplored. In this work, we propose QuantFL, a sustainable FL framework that leverages pre-trained initialisation to enable aggressive, computationally lightweight quantisation. We demonstrate that pre-training naturally concentrates update statistics, allowing us to use memory-efficient bucket quantisation without the energy-intensive overhead of complex error-feedback mechanisms. On MNIST and CIFAR-100, QuantFL reduces total communication by 40\% ($\simeq40\%$ total-bit reduction with full-precision downlink; $\geq80\%$ on uplink or when downlink is quantised) while matching or exceeding uncompres
arXiv:2603.17507v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are increasingly available on edge devices, their potential to reduce the energy overhead of fine-tuning remains underexplored. In this work, we propose QuantFL, a sustainable FL framework that leverages pre-trained initialisation to enable aggressive, computationally lightweight quantisation. We demonstrate that pre-training naturally concentrates update statistics, allowing us to use memory-efficient bucket quantisation without the energy-intensive overhead of complex error-feedback mechanisms. On MNIST and CIFAR-100, QuantFL reduces total communication by 40\% ($\simeq40\%$ total-bit reduction with full-precision downlink; $\geq80\%$ on uplink or when downlink is quantised) while matching or exceeding uncompressed baselines under strict bandwidth budgets; BU attains 89.00\% (MNIST) and 66.89\% (CIFAR-100) test accuracy with orders of magnitude fewer bits. We also account for uplink and downlink costs and provide ablations on quantisation levels and initialisation. QuantFL delivers a practical, "green" recipe for scalable training on battery-constrained IoT networks.
Executive Summary
QuantFL, a novel Federated Learning framework, leverages pre-trained models to reduce the energy overhead of fine-tuning, thereby decreasing the carbon footprint of IoT devices. By employing aggressive quantisation, QuantFL achieves significant reductions in total communication (40%) and energy consumption, while maintaining comparable accuracy to uncompressed baselines. This approach has the potential to revolutionise scalable training on battery-constrained IoT networks, making it a practical and sustainable solution for the future of edge computing.
Key Points
- ▸ QuantFL leverages pre-trained models for initialisation and fine-tuning
- ▸ Aggressive quantisation and memory-efficient bucket quantisation reduce energy overhead
- ▸ Significant reductions in total communication (40%) and energy consumption achieved
- ▸ Comparable accuracy maintained to uncompressed baselines under strict bandwidth budgets
Merits
Strength in Reducing Carbon Footprint
QuantFL's ability to reduce the carbon footprint of IoT devices by leveraging pre-trained models and aggressive quantisation is a significant merit. This approach addresses the growing concern of energy consumption in Federated Learning and contributes to a more sustainable future for edge computing.
Scalability and Efficiency
QuantFL's design enables scalable training on battery-constrained IoT networks, making it a practical solution for edge computing. The framework's efficiency in reducing energy consumption and communication overhead makes it a valuable contribution to the field of Federated Learning.
Demerits
Limited Evaluation on Real-World Scenarios
The evaluation of QuantFL is primarily focused on synthetic datasets (MNIST and CIFAR-100), which may not accurately reflect real-world scenarios. Further evaluation on real-world datasets and edge devices is necessary to fully assess the framework's performance and potential.
Quantisation Levels and Initialisation Ablations
While the authors provide some ablations on quantisation levels and initialisation, a more comprehensive exploration of these parameters is necessary to understand their impact on the framework's performance and to ensure optimal results.
Expert Commentary
The authors have made a significant contribution to the field of Federated Learning by proposing a novel framework (QuantFL) that leverages pre-trained models and aggressive quantisation to reduce energy consumption and communication overhead. While the evaluation is primarily focused on synthetic datasets, the framework's design and results demonstrate its potential as a practical and sustainable solution for edge computing. However, further evaluation on real-world scenarios and more comprehensive exploration of quantisation levels and initialisation are necessary to fully assess the framework's performance and potential.
Recommendations
- ✓ Further evaluation on real-world datasets and edge devices is necessary to fully assess the framework's performance and potential
- ✓ More comprehensive exploration of quantisation levels and initialisation is necessary to ensure optimal results and to understand their impact on the framework's performance