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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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Cui, T., Shi, Y., Li, W., Ding, R., & Wang, Q. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/3466867
Improving crowdsourced label quality by peer-to-peer federated learning
Lu, X., Qian, J., Wang, C., Yan, D., & Zhang, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06686-w
Hussain, G. K. J., & Manoj, G. (2025) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla63829.2025.11040496
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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