期刊
IEEE INTERNET OF THINGS JOURNAL
卷 6, 期 6, 页码 10700-10714出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2940820
关键词
Blockchain; contract theory; federated learning; mobile networks; reputation; security and privacy
资金
- WASP/NTU [M4082187 (4080)]
- Singapore Ministry of Education (MOE) Tier 1 [2017-T1-002-007 RG122/17]
- MOE Tier 2 [MOE2014-T2-2-015 ARC4/15]
- SingaporeIsrael NRF-ISF [NRF2015-NRF-ISF001-2277]
- EMA Energy Resilience [NRF2017EWT-EP003-041]
- NSFC [61973087, 61703113]
Federated learning is an emerging machine learning technique that enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only model updates without uploading the raw training data. This technique provides a promising privacy preservation for mobile devices while simultaneously ensuring high learning performance. The majority of existing work has focused on designing advanced learning algorithms with an aim to achieve better learning performance. However, the challenges, such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. These challenges have hindered the widespread adoption of federated learning. To address the above challenges, in this article, we first introduce reputation as the metric to measure the reliability and trustworthiness of the mobile devices. We then design a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model. We also leverage the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties in a decentralized manner. Moreover, we propose an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning. Numerical results clearly indicate that the proposed schemes are efficient for reliable federated learning in terms of significantly improving the learning accuracy.
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