The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A Crowdsourcing Framework for On-Device Federated Learning
Open accessAbstract
Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.
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“However, the works in [5] and [6] do not discuss the important challenges pertaining to incentive design and network optimization under edge-based FL.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [5]
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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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
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Federated Optimization in Heterogeneous Networks
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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
Mobile crowdsensing: current state and future challenges
Ganti, R., Ye, F., & Lei, H. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.6069707 · Cited by 2,075
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563
Elementary Numerical Analysis: An Algorithmic Approach
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2017) · arXiv · doi:10.48550/arxiv.1712.01887 · Cited by 1,134
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A survey on federated learning
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899
A review of applications in federated learning
Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503
“Given a crowdsourcing/crowdsensing system, the work in [96] focuses on design-”
BackgroundMethodInfluentialFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892
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Metadata
- Authors
- Pandey, S. R., Tran, N. H., Bennis, M., Tun, Y. K., Manzoor, A., & Hong, C. S.
- Affiliations
- Kyung Hee University (KR)
- The University of Sydney (AU)
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2020-05
- Volume
- 19
- Issue
- 5
- Pages
- 3241-3256
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W2989120265
- Cited by
- 318
- References
- 49
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 59 · 2023: 57 · 2022: 71 · 2021: 62 · 2020: 20
- FWCI
- 20.62
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Wireless Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (2019-0-01287)
- Evolvable Deep Learning Model Generation Platform for Edge Computing
- National Research Foundation of Korea (NRF) funded by the Korea Government (NRF-2017R1A2A2A05000995)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, crowdsourcing, on-device learning, communication efficiency, utility maximization, Stackelberg game, incentive mechanisms, admission control, client participation, reward allocation, local accuracy
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