S. R. Pandey, N. H. Tran, M. Bennis, Y. K. Tun, A. Manzoor and C. S. Hong, "A Crowdsourcing Framework for On-Device Federated Learning," in IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3241-3256, May 2020, doi: 10.1109/TWC.2020.2971981
A crowdsourcing framework for on-device federated learning
|Author:||Pandey, Shashi Raj1; Tran, Nguyen H.2,3; Bennis, Mehdi4,3;|
1Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Rep. of Korea
2School of Computer Science, The University of Sydney, NSW 2006, Australia
3Department of Computer Science and Engineering, Kyung Hee University, Seoul 17104, South Korea
4Center for Wireless Communications, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020060440593
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-06-04
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.
IEEE transactions on wireless communications
|Pages:||3241 - 3256|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01287, Evolvable Deep Learning Model Generation Platform for Edge Computing) and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (NRF-2017R1A2A2A05000995).
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