Blockchain-based Federated Recommendation with Incentive Mechanism
Jianhai Chen, Yanlin Wu, Dazhong Rong, Guoyao Yu, Lingqi Jiang, Zhenguang Liu, Peng Zhou, Rui Shen
TL;DR
The paper addresses the challenge of building trustworthy, privacy-preserving federated recommendation systems that are also cost-efficient. It proposes a blockchain-based framework around NeuMF with FedAvg, augmented by a D3QN-driven reverse auction to select high-quality client data sources and on-chain evidence storage for verifiability. Empirical results on MovieLens 1M show that the incentive mechanism increases social surplus by up to 54.9% and improves recommendation metrics, while reducing the number of participating clients and speeding convergence. This work offers a practical path toward harmonizing privacy, security, performance, and economic incentives in federated recommendations with verifiable trust.
Abstract
Nowadays, federated recommendation technology is rapidly evolving to help multiple organisations share data and train models while meeting user privacy, data security and government regulatory requirements. However, federated recommendation increases customer system costs such as power, computational and communication resources. Besides, federated recommendation systems are also susceptible to model attacks and data poisoning by participating malicious clients. Therefore, most customers are unwilling to participate in federated recommendation without any incentive. To address these problems, we propose a blockchain-based federated recommendation system with incentive mechanism to promote more trustworthy, secure, and efficient federated recommendation service. First, we construct a federated recommendation system based on NeuMF and FedAvg. Then we introduce a reverse auction mechanism to select optimal clients that can maximize the social surplus. Finally, we employ blockchain for on-chain evidence storage of models to ensure the safety of the federated recommendation system. The experimental results show that our proposed incentive mechanism can attract clients with superior training data to engage in the federal recommendation at a lower cost, which can increase the economic benefit of federal recommendation by 54.9\% while improve the recommendation performance. Thus our work provides theoretical and technological support for the construction of a harmonious and healthy ecological environment for the application of federal recommendation.