| Title | A Privacy-Preserving Incentive Mechanism for Federated Cloud-Edge Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Tianyu, Di, Boya, Wang, Shupeng, Song, Lingyang |
| Conference Name | 2021 IEEE Global Communications Conference (GLOBECOM) |
| Keywords | Cloud-edge computing, Computational modeling, control theory, Data models, data privacy, Differential privacy, federated learning, Games, Human Behavior, incentive mechanism, Learning systems, optimal control, privacy, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | The federated learning scheme enhances the privacy preservation through avoiding the private data uploading in cloud-edge computing. However, the attacks against the uploaded model updates still cause private data leakage which demotivates the privacy-sensitive participating edge devices. Facing this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the edge devices are motivated to actively contribute to the updated model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We formulate the incentive design problem as a three-layer Stackelberg game, where the server-device interaction is further formulated as a contract design problem. Extensive numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility. |
| DOI | 10.1109/GLOBECOM46510.2021.9685615 |
| Citation Key | liu_privacy-preserving_2021 |
A Privacy-Preserving Incentive Mechanism for Federated Cloud-Edge Learning