IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation
文献类型：期刊论文
| 作者 | Ying, Xukai3; Yan, Keyang2; Gao, Xizhang1; Huang, Jie3 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2026-03-30 |
| 卷号 | 16期号:1页码:10567 |
| 关键词 | Federated learning Knowledge distillation Blockchain Incentive mechanism |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-026-46234-1 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Knowledge Distillation-based Federated Learning (KD-FL) has garnered significant attention as one of the core technical pathways for next-generation Federated Learning (FL), owing to its communication efficiency, privacy preservation, and strong robustness. Meanwhile, to further reduce reliance on a central server, blockchain-enabled KD-FL architectures have become a research hotspot. However, designing an effective incentive mechanism that encourages participants to consistently contribute high-quality knowledge remains a fundamental challenge for ensuring the system's long-term sustainability. To address this issue, this paper proposes an Incentive Mechanism for decentralized FL based on Knowledge Distillation (IMFLKD). First, we design a two-stage evaluation method, combining smart contract-based label aggregation and peer-wise comparison, that enables accurate client model quality estimation and fair reward allocation without increasing time complexity. Second, we establish a multi-dimensional dynamic reputation system based on the Subjective Logic model, incorporating metrics such as data quality, activity level, and stability to identify high-value participants and incentivize sustained, high-quality contributions across multiple FL rounds rather than short-term opportunistic behavior. Finally, we integrate these components into a decentralized, blockchain-enabled KD-FL framework. Experimental results demonstrate that IMFLKD achieves superior performance in contribution assessment accuracy, computational overhead, and resilience against malicious attacks, showcasing strong practicality and reliability. |
| URL标识 | 查看原文 |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001730899200009 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221498]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Huang, Jie |
| 作者单位 | 1.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Rutgers State Univ, Sch Arts & Sci, New Brunswick, NJ 08901 USA; 3.Zhejiang Univ Sci & Technol, Dept Comp Sci, Hangzhou 310023, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ying, Xukai,Yan, Keyang,Gao, Xizhang,et al. IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation[J]. SCIENTIFIC REPORTS,2026,16(1):10567. |
| APA | Ying, Xukai,Yan, Keyang,Gao, Xizhang,&Huang, Jie.(2026).IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation.SCIENTIFIC REPORTS,16(1),10567. |
| MLA | Ying, Xukai,et al."IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation".SCIENTIFIC REPORTS 16.1(2026):10567. |
入库方式： OAI收割
来源：地理科学与资源研究所
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