Privacy-Preserving Blockchain-Based Federated Learning for Marine Internet of Things
Bibliographic Data
| ID | 22108479 |
|---|---|
| Authors | Zhenquan Qin (0000-0002-5804-276X, Dalian University of Technology), Ye Jin (0000-0002-2147-127X, Dalian University of Technology), Jin Ye (0000-0001-9255-2547), Jie Meng (0000-0002-0512-1704, Dalian University of Technology), Bingxian Lu (0000-0002-4378-6539, Dalian University of Technology), Lei Wang (0000-0001-5326-565X, Dalian University of Technology) |
| Year | 2022 |
| Volume | 9 |
| Issue | 1 |
| Pages | 159-173 |
| Publication date | 2022-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3100258 |
| OpenAlex | W3189627679 |
| Language | EN |
| Citations received | 3 |
| References cited | 35 |
The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment
Big data · Blockchain · Cloud computing · Computer security · Data mining · Distributed computing · Edge computing · Enhanced Data Rates for GSM Evolution · Internet of Things · Reputation · The Internet · World Wide Web · Blockchain Technology Applications and Security · Computer Science · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Architecture · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 1.5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |