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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Cites
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Federated Learning: Strategies for Improving Communication Efficiency
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
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Yao, H., & Mai, T. (2025) · Wireless networks · doi:10.1007/978-3-031-96444-2_5
A Game Theory Reward Model for Federated Learning with Probabilistic Verification
Auricchio, G., Clough, H. J., Ho, C., Bian, K., Dong, C., Yang, K., & Zhang, J. (2024) · doi:10.1145/3719545.3721106
Privacy-Preserving Incentive Mechanism Design for Wireless Federated Learning
Chen, X., Huang, H., Zhang, S., & Sun, Y. (2025) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops67674.2025.11162339
A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning
Nguyen, T. L., & Pham, Q. (2025) · Globecom · doi:10.1109/globecom59602.2025.11432061
FBCCNet: a Bayesian perspective of federated learning with crowdsourced annotations on client side
Zhu, H., Wang, X., & Jin, Y. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02182-3
Multi-dimensional Resource Adaptation for Hierarchical Federated Learning
Zhang, W., Yang, D. H., Gao, S., Zhang, H., & Xuemin (2026) · Wireless networks · doi:10.1007/978-3-032-07667-0_3
Li, Q., Zhao, H., Wang, Q., Zhang, W., Chen, Y., & Hu, Z. (2026) · IET Communications · doi:10.1049/cmu2.70139
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FLET: Game-Theoretic Free-Riding Mitigation via Test Tasks in Federated Learning
Guo, S., Wang, Y., Su, Z., Pan, Y., Luan, T. H., & Luo, X. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3668944
A Learning-Based Contract Mechanism for Privacy-Aware Incentives in UAV-Assisted Federated Learning
Zhu, C., Li, X., & Zhu, X. (2026) · Intelligent and Converged Networks · doi:10.23919/icn.2026.0002
A novel approach for fair incentive social physical data based on blockchain-federated learning
Mohamed, H. G., Tran-Huy, H., Hoang-Thu, T., Qaddara, I., Choi, B. J., Alshehri, A. H., Ahad, I., & Liu, H. (2026) · Frontiers in Physics · doi:10.3389/fphy.2026.1778336
Crowdsourcing Feature Selection via a Distributed Evolutionary Algorithm
Liang, S.-R., Wei, F.-F., Lin, Q., Luo, W.-J., & Chen, W. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3688040
SentinelFL: Noise-Driven Global Model Protection Against Model Theft in Federated Learning
Kwon, D., & Choi, B. J. (2026) · International Conference on Information Networking · doi:10.1109/icoin68469.2026.11480621
Democratizing Federated Learning with Blockchain and Multi-task Peer Prediction
Witt, L., Samek, W., Toyoda, K., & Li, D. (2025) · Adaptation, learning, and optimization · doi:10.1007/978-3-032-12362-6_8
Prity, J. A., & Mahdi, A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545667
Decentralized federated learning with credit-driven incentive mechanism for wind power prediction
Rong, J., Cheng, Y., Li, H., Jiang, F., & Yang, Y. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115768
Si, H., Liu, Y., Guo, X., Boateng, G. O., Ansari, N., & Wang, Y. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2026.3738508
A Comprehensive Review on Federated and Incremental Learning in Spatial Crowdsourcing Systems
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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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