Cited By
View all- Bian JXu J(2024)Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile RelayingIEEE Transactions on Vehicular Technology10.1109/TVT.2024.338406173:7(10668-10680)Online publication date: Jul-2024
ICB FL: Implicit Class Balancing Towards Fairness in Federated Learning
Pages 135 - 142
Abstract
Federated learning (FL) is a promising machine learning paradigm that allows many clients jointly train a model without sharing the raw data. As the standard FL has been designed from the server’s perspective, the unfairness issue may occur throughout the whole learning process including the global model optimization phase. Some existing works have attempted this issue to guarantee the global model achieves a similar accuracy across different classes (i.e., labels), but failed to consider the implicit classes (different representations of one label) under them in which the fairness issue persists. In this paper, we focus on the fairness issue in the global model optimization phase and mitigate the research gap by introducing the Implicit Class Balancing (ICB) Federated Learning framework with Single Class Training Scheme (SCTS). In ICB FL, the server first broadcasts the current global model and assigns a particular class (label) for each client. Then, each client locally trains the model only with the assigned class data (SCTS) and sends the gradient back to the server. The server subsequently performs unsupervised learning to identify the implicit classes and generates the balanced weight for each client. Finally, the server averages the gradient received with weights, and updates the global model. We evaluate our ICB FL in three datasets, and the experimental results show that our ICB FL can effectively enhance fairness across explicit and implicit classes.
References
[1]
Patrick Bolton and Mathias Dewatripont. 2004. Contract theory. MIT press.
[2]
Li Deng. 2012. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine 29, 6 (2012), 141–142.
[3]
John A Hartigan and Manchek A Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics) 28, 1(1979), 100–108.
[4]
Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, and Albert Y. Zomaya. 2021. An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee. IEEE Transactions on Parallel and Distributed Systems 32, 7 (2021), 1552–1564. https://doi.org/10.1109/TPDS.2020.3040887
[5]
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, 2021. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14, 1–2(2021), 1–210.
[6]
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim. 2019. Incentive design for efficient federated learning in mobile networks: A contract theory approach. In 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). IEEE, 1–5.
[7]
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.
[8]
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2019. Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations.
[9]
Yanli Li, Abubakar Sadiq Sani, Dong Yuan, and Wei Bao. 2022. Enhancing Federated Learning Robustness Through clustering Non-IID Features. In Proceedings of the Asian Conference on Computer Vision. 41–55.
[10]
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.
[11]
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019. Agnostic federated learning. In International Conference on Machine Learning. PMLR, 4615–4625.
[12]
Zhendong Song, Hongguang Sun, Howard H. Yang, Xijun Wang, Yan Zhang, and Tony Q. S. Quek. 2022. Reputation-Based Federated Learning for Secure Wireless Networks. IEEE Internet of Things Journal 9, 2 (2022), 1212–1226. https://doi.org/10.1109/JIOT.2021.3079104
[13]
MA Syakur, BK Khotimah, EMS Rochman, and Budi Dwi Satoto. 2018. Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP conference series: materials science and engineering, Vol. 336. IOP Publishing, 012017.
[14]
Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li. 2020. Optimizing federated learning on non-iid data with reinforcement learning. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 1698–1707.
[15]
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu. 2021. Addressing class imbalance in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 10165–10173.
[16]
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747(2017).
[17]
Zhaoping Xiong, Ziqiang Cheng, Xinyuan Lin, Chi Xu, Xiaohong Liu, Dingyan Wang, Xiaomin Luo, Yong Zhang, Hualiang Jiang, Nan Qiao, 2022. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches. Science China Life Sciences 65, 3 (2022), 529–539.
[18]
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2(2019), 1–19.
[19]
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 13, 3(2019), 1–207.
[20]
Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar. 2020. Federated learning with only positive labels. In International Conference on Machine Learning. PMLR, 10946–10956.
[21]
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang. 2020. A fairness-aware incentive scheme for federated learning. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 393–399.
[22]
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web. 1171–1180.
[23]
Qunsong Zeng, Yuqing Du, Kaibin Huang, and Kin K Leung. 2021. Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing. IEEE Transactions on Wireless Communications 20, 12(2021), 7947–7962.
[24]
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, and Mohan Kankanhalli. 2020. Hierarchically fair federated learning. arXiv preprint arXiv:2004.10386(2020).
[25]
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin. 2021. Federated learning on non-IID data: A survey. Neurocomputing 465(2021), 371–390. https://doi.org/10.1016/j.neucom.2021.07.098
Index Terms
- ICB FL: Implicit Class Balancing Towards Fairness in Federated Learning
Index terms have been assigned to the content through auto-classification.
Recommendations
Improve individual fairness in federated learning via adversarial training
Highlights- We present an adversarial training approach for training FL models that satisfy individual fairness without requiring direct access to training data or the underlying data distribution, thereby protecting user privacy at the same time.
AbstractFederated learning (FL) has been widely investigated these years. Since FL will be universally applied in the real world, the fairness issue involved is worthy of attention, while there are few relevant studies. Unlike previous work on group ...
Inferring Class-Label Distribution in Federated Learning
AISec'22: Proceedings of the 15th ACM Workshop on Artificial Intelligence and SecurityFederated Learning (FL) has become a popular distributed learning method for training classifiers by using data that are private to individual clients. The clients´ data are typically assumed to be confidential, but their heterogeneity and potential ...
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningTraditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set. In this paper, we focus on a more general yet practical setting, non-...
Comments
Information & Contributors
Information
Published In
January 2023
272 pages
ISBN:9798400700057
DOI:10.1145/3579375
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 March 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ACSW 2023
ACSW 2023: 2023 Australasian Computer Science Week
January 30 - February 3, 2023
VIC, Melbourne, Australia
Acceptance Rates
Overall Acceptance Rate 61 of 141 submissions, 43%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 65Total Downloads
- Downloads (Last 12 months)19
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Feb 2025
Other Metrics
Citations
Cited By
View all- Bian JXu J(2024)Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile RelayingIEEE Transactions on Vehicular Technology10.1109/TVT.2024.338406173:7(10668-10680)Online publication date: Jul-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format