skip to main content
10.1145/3673038.3673084acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicppConference Proceedingsconference-collections
research-article
Open access

Federated Edge Learning with Blurred or Pseudo Data Sharing

Published: 12 August 2024 Publication History

Abstract

Edge servers and mobile devices are often assigned a large number of computing tasks. However, the data involved in computing tasks is often sensitive in terms of privacy. Our initial proposal is a federated edge learning strategy based on real-world scenarios, which combines blurred data or pseudo shared data. Federated learning is used to train device models with the aim of protecting privacy while enabling mobile devices to more effectively utilize data for decision-making. In the case of limited energy on mobile devices, we propose a federated edge learning algorithm with blurred data sharing. This algorithm can generate more accurate models by uploading partially blurred data. In order to further improve model accuracy and protect privacy of mobile devices, we propose a federated edge learning algorithm with pseudo data sharing based on dataset distillation and generative adversarial networks (GANs) in scenarios with relatively sufficient energy. The experimental results on several traditional datasets show that our proposed algorithms outperform traditional algorithms in terms of accuracy and energy consumption.

References

[1]
Gizem Akman, Philip Ginzboorg, Mohamed Taoufiq Damir, and Valtteri Niemi. 2023. Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility. Comput. 12, 1 (2023), 2. https://doi.org/10.3390/computers12010002
[2]
Hankyul Baek, Won Joon Yun, Yunseok Kwak, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, and Joongheon Kim. 2022. Joint superposition coding and training for federated learning over multi-width neural networks. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 1729–1738.
[3]
Mehdi Bolourian and Hamed Shah-Mansouri. 2023. Energy-Efficient Task Offloading for Three-Tier Wireless-Powered Mobile-Edge Computing. IEEE Internet Things J. 10, 12 (2023), 10400–10412.
[4]
Xingjian Cao, Gang Sun, Hongfang Yu, and Mohsen Guizani. 2022. PerFED-GAN: Personalized federated learning via generative adversarial networks. IEEE Internet of Things Journal 10, 5 (2022), 3749–3762.
[5]
Qimei Chen, Xiaoxia Xu, Zehua You, Hao Jiang, Jun Zhang, and Fei-Yue Wang. 2021. Communication-Efficient Federated Edge Learning for NR-U based IIoT Networks. IEEE Internet of Things Journal (2021).
[6]
Anda Cheng, Peisong Wang, Xi Sheryl Zhang, and Jian Cheng. 2022. Differentially Private Federated Learning with Local Regularization and Sparsification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10122–10131.
[7]
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021. Exploiting shared representations for personalized federated learning. (2021), 2089–2099.
[8]
Cailian Deng, Xuming Fang, and Xianbin Wang. 2023. UAV-Enabled Mobile-Edge Computing for AI Applications: Joint Model Decision, Resource Allocation, and Trajectory Optimization. IEEE Internet Things J. 10, 7 (2023), 5662–5675. https://doi.org/10.1109/JIOT.2022.3151619
[9]
Ahmed Roushdy Elkordy and A Salman Avestimehr. 2022. Heterosag: Secure aggregation with heterogeneous quantization in federated learning. IEEE Transactions on Communications 70, 4 (2022), 2372–2386.
[10]
Chunrong He, Guiyan Liu, Songtao Guo, and Yuanyuan Yang. 2022. Privacy-Preserving and Low-Latency Federated Learning in Edge Computing. IEEE Internet of Things Journal (2022).
[11]
Rui Hu, Yanmin Gong, and Yuanxiong Guo. 2020. Federated learning with sparsification-amplified privacy and adaptive optimization. arXiv preprint arXiv:2008.01558 (2020).
[12]
Youqiang Hu, Hejiao Huang, and Nuo Yu. 2022. Resource Optimization and Device Scheduling for Flexible Federated Edge Learning with Tradeoff Between Energy Consumption and Model Performance. Mobile Networks and Applications 27, 5 (2022), 2118–2137.
[13]
Xuwei Huang and Gaofei Huang. 2023. Joint Optimization of Energy and Task Scheduling in Wireless-Powered IRS-Assisted Mobile-Edge Computing Systems. IEEE Internet Things J. 10, 12 (2023), 10997–11013.
[14]
Lin Jia, Zhi Zhou, Fei Xu, and Hai Jin. 2021. Cost-Efficient Continuous Edge Learning for Artificial Intelligence of Things. IEEE Internet of Things Journal 9, 10 (2021), 7325–7337.
[15]
Zhida Jiang, Yang Xu, Hongli Xu, Zhiyuan Wang, Chunming Qiao, and Yangming Zhao. 2022. FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 767–779.
[16]
Qiushi Li, Ju Ren, Yuezhi Zhou, and Yaoxue Zhang. 2022. Privacy-Preserving DNN Model Authorization against Model Theft and Feature Leakage. In IEEE International Conference on Communications, ICC 2022, Seoul, Korea, May 16-20, 2022. IEEE, 5633–5638.
[17]
Robert P McIntosh. 1967. An index of diversity and the relation of certain concepts to diversity. Ecology 48, 3 (1967), 392–404.
[18]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273–1282.
[19]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.
[20]
Xiaopeng Mo and Jie Xu. 2021. Energy-efficient federated edge learning with joint communication and computation design. Journal of Communications and Information Networks 6, 2 (2021), 110–124.
[21]
Pavana Prakash, Jiahao Ding, Rui Chen, Xiaoqi Qin, Minglei Shu, Qimei Cui, Yuanxiong Guo, and Miao Pan. 2022. IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization. IEEE Internet of Things Journal 9, 15 (2022), 13638–13650.
[22]
Sujie Shao, Lili Su, Qinghang Zhang, Shuang Wu, Shaoyong Guo, and Feng Qi. 2023. Multi task dynamic edge-end computing collaboration for urban Internet of Vehicles. Comput. Networks 227 (2023), 109690. https://doi.org/10.1016/j.comnet.2023.109690
[23]
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. 2022. Fedproto: Federated prototype learning across heterogeneous clients. 36, 8 (2022), 8432–8440.
[24]
Nguyen H Tran, Wei Bao, Albert Zomaya, Minh NH Nguyen, and Choong Seon Hong. 2019. Federated learning over wireless networks: Optimization model design and analysis. In IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE, 1387–1395.
[25]
Yichen Wan, Youyang Qu, Longxiang Gao, and Yong Xiang. 2022. Privacy-preserving blockchain-enabled federated learning for b5g-driven edge computing. Computer Networks 204 (2022), 108671.
[26]
Jin Wang, Jia Hu, Geyong Min, Wenhan Zhan, Albert Y Zomaya, and Nektarios Georgalas. 2021. Dependent task offloading for edge computing based on deep reinforcement learning. IEEE Trans. Comput. 71, 10 (2021), 2449–2461.
[27]
Jian Wang, Hongchang Ke, Xuejie Liu, and Hui Wang. 2022. Optimization for computational offloading in multi-access edge computing: A deep reinforcement learning scheme. Computer Networks 204 (2022), 108690.
[28]
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018. Dataset distillation. arXiv preprint arXiv:1811.10959 (2018).
[29]
Weilong Wang, Yingjie Wang, Yan Huang, Chunxiao Mu, Zice Sun, Xiangrong Tong, and Zhipeng Cai. 2022. Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing. Computer Networks 215 (2022), 109206.
[30]
Xinghan Wang, Xiaoxiong Zhong, Jiahong Ning, Hangfan Li, Tingting Yang, and Yuanyuan Yang. 2022. Two-Stage Coded Federated Edge Learning: A Dynamic Partial Gradient Coding Perspective. arXiv preprint arXiv:2205.07939 (2022).
[31]
Zhilin Wang, Qin Hu, and Zehui Xiong. 2022. Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing. arXiv preprint arXiv:2206.02243 (2022).
[32]
Qiong Wu, Xu Chen, Zhi Zhou, and Junshan Zhang. 2020. Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring. IEEE Transactions on Mobile Computing (2020).
[33]
Jianlong Xu, Jian Lin, Wei Liang, and Kuan-Ching Li. 2022. Privacy preserving personalized blockchain reliability prediction via federated learning in IoT environments. Cluster Computing 25, 4 (2022), 2515–2526.
[34]
Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, and Mohammad Shikh-Bahaei. 2020. Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20, 3 (2020), 1935–1949.
[35]
Xiaotong Yuan and Ping Li. 2022. On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. (2022).
[36]
Hao Zhang, Tingting Wu, Siyao Cheng, and Jie Liu. 2022. Aperiodic local SGD: Beyond local SGD. (2022), 1–10.
[37]
Hao Zhang, Tingting Wu, Siyao Cheng, and Jie Liu. 2022. Fedcos: A scene-adaptive enhancement for federated learning. IEEE Internet of Things Journal 10, 5 (2022), 4545–4556.
[38]
Huan Zhou, Kai Jiang, Xuxun Liu, Xiuhua Li, and Victor CM Leung. 2021. Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing. IEEE Internet of Things Journal 9, 2 (2021), 1517–1530.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICPP '24: Proceedings of the 53rd International Conference on Parallel Processing
August 2024
1279 pages
ISBN:9798400717932
DOI:10.1145/3673038
This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2024

Check for updates

Author Tags

  1. blurred data sharing
  2. dataset distillation
  3. energy optimization
  4. federated edge learning

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICPP '24

Acceptance Rates

Overall Acceptance Rate 91 of 313 submissions, 29%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 61
    Total Downloads
  • Downloads (Last 12 months)61
  • Downloads (Last 6 weeks)30
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media