Name
Title | ||
|---|---|---|
Hybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing |
Abstract | ||
|---|---|---|
By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2020.3028101 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Auction,blockchain,edge computing,Internet of Things (IoT),trade market | Journal | 8 |
Issue | ISSN | Citations |
4 | 2327-4662 | 7 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sizheng Fan | 1 | 7 | 0.40 |
| Hongbo Zhang | 2 | 14 | 5.68 |
| Yuchen Zeng | 3 | 7 | 0.40 |
| Wei Cai | 4 | 175 | 39.84 |