A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security
Abstract
Training high-quality machine learning models on distributed systems is a critical issue to achieve edge intelligence in wireless communications. Conventional data-driven machine learning approaches are infeasible due to non-IID data caused by privacy issues and the limited communication resources in wireless networks. Besides, considering the complex user identities, the training process also faces the challenges of Byzantine devices, which can inject poisoning information into models. In this paper, we propose a two-step federated learning framework, robust federated augmentation and distillation (RFA-RFD), to enable privacy-preserving, communication-efficient, and Byzantine-tolerant on-device machine learning in wireless communications. RFA is a method to tackle the problem of non-IID local data, which firstly trains local data generators on edge devices, then trains a global generator in the cloud server according to the IID dataset generated by the uploaded local generators, and finally, devices rectify non-IID dataset by downloading the global generator. After obtaining IID local data in edge devices, RFD is implemented to improve the performance of local models, in which devices only share the local information of models' outputs to reduce communication overhead. By employing a detection and discard mechanism in both RFA and RFD, our framework achieves robustness to the influence of Byzantine devices. Experiments show the effectiveness of RFA-RFD on preserving privacy, correcting non-IID data, reducing communication overhead, and resisting Byzantine devices, without much loss of accuracy compared with existing state-of-the-art methods.
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ML-based intrusion detection algorithms can be used for identification of adversary actions.
“Moreover, it has been observed that ML-based intrusion detection algorithms can also be used for the identification of adversary actions [32], [33].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [33]
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · 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
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863 · see citing works
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works
Isolation-Based Anomaly Detection
Liu, F. T., Ting, K. M., & Zhou, Z. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2133360.2133363 · Cited by 2,112 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725 · see citing works
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130 · see citing works
Auto-Encoding Variational Bayes
Chen, Y.-K., Liu, J., Peng, L., Wu, Y., Xu, Y., & Zhang, Z. (2024) · Cambridge Explorations in Arts and Sciences · doi:10.61603/ceas.v2i1.33 · Cited by 939 · see citing works
Self-citationJeong, E., Oh, S., Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2018) · arXiv · doi:10.48550/arxiv.1811.11479 · Cited by 465 · see citing works
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.07210 · Cited by 312 · see citing works
Poisoning Attack in Federated Learning using Generative Adversarial Nets
Zhang, J., Chen, J., Wu, D., Chen, B., & Yu, S. (2019) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase.2019.00057 · Cited by 216 · see citing works
Self-citationByzantine-Robust Federated Machine Learning through Adaptive Model AveragingPreprint
Muñoz-González, L., Co, K. T., & Lupu, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05125 · Cited by 153 · see citing works
Distance-based outlier detection
Orair, G. H., Teixeira, C. H. C., Meira, W., Wang, Y., & Parthasarathy, S. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1921021 · Cited by 108 · see citing works
AggregaThor: Byzantine Machine Learning via Robust Gradient Aggregation
Damaskinos, G., Guirguis, A., & Rouault, S. (2019) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5281/zenodo.2548778 · Cited by 43 · see citing works
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous TrainingPreprint
Li, C., Li, R., Wang, H., Li, Y., Zhou, P., Guo, S., & Li, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07848 · Cited by 12 · see citing works
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
- Authors
- Wen, H., Wu, Y., Yang, C., Duan, H., & Yu, S.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- University of Technology Sydney (AU)
- Venue
- IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 653-658
- OpenAlex
- W3048392890
- Cited by
- 41
- References
- 20
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 9 · 2023: 5 · 2022: 9 · 2021: 4 · 2020: 1
- FWCI
- 1.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Keywords
- federated learning, wireless communication, non-IID data, data augmentation, knowledge distillation, Byzantine attacks, data poisoning, privacy preservation, communication efficiency, edge AI, on-device learning
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