Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
Open accessAbstract
Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models. Gradient quantization is an effective way of reducing the number of bits required to communicate each model update, albeit at the cost of having a higher error floor due to the higher variance of the stochastic gradients. In this work, we propose an adaptive quantization strategy called AdaQuantFL that aims to achieve communication efficiency as well as a low error floor by changing the number of quantization levels during the course of training. Experiments on training deep neural networks show that our method can converge in much fewer communicated bits as compared to fixed quantization level setups, with little or no impact on training and test accuracy.
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Compression methods usually add to the error floor of the training objective as they increase the variance of the updates [84].
“However, such compression methods usually add to the error floor of the training objective as they increase the variance of the updates [84].”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [84]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., & Pedarsani, R. (2019) · arXiv · doi:10.48550/arxiv.1909.13014 · Cited by 587 · see citing works
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Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Towards Accurate Post-Training Quantization for Vision Transformer
Ding, Y., Qin, H., Yan, Q., Chai, Z., Liu, J., Wei, X., & Liu, X. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3547826 · Cited by 79 · see citing works
- Authors
- Jhunjhunwala, D., Gadhikar, A., Joshi, G., & Eldar, Y. C.
- Affiliations
- Carnegie Mellon University (US)
- Weizmann Institute of Science (IL)
- Venue
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2021-06-06
- Pages
- 3110-3114
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3127479988
- Cited by
- 126
- References
- 4
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 40 · 2023: 18 · 2022: 20 · 2021: 4
- FWCI
- 9.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, gradient quantization, adaptive quantization, communication efficiency, model updating, quantization levels, deep neural network, error floor
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