Abstract 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.
Ying et al. (Mon,) studied this question.