基于区块链的联邦推荐与激励机制
Blockchain-based Federated Recommendation with Incentive Mechanism
AI总结：
本文提出基于区块链的联邦推荐系统，结合NeuMF与FedAvg，通过反向拍卖激励机制选择最优客户端，在提升推荐性能的同时将经济效益提高54.9%。
摘要：
如今，联邦推荐技术正在快速发展，以帮助多个组织在满足用户隐私、数据安全和政府监管要求的同时共享数据并训练模型。然而，联邦推荐增加了客户系统的成本，例如电力、计算和通信资源。此外，联邦推荐系统也容易受到参与恶意客户端的模型攻击和数据投毒的影响。因此，大多数客户不愿意在没有任何激励的情况下参与联邦推荐。为了解决这些问题，我们提出了一种基于区块链的联邦推荐系统，该系统带有激励机制，以促进更可信、更安全、更高效的联邦推荐服务。首先，我们构建了一个基于NeuMF和FedAvg的联邦推荐系统。然后，我们引入了一种反向拍卖机制来选择能够最大化社会剩余的最优客户端。最后，我们采用区块链对模型进行链上证据存储，以确保联邦推荐系统的安全性。实验结果表明，我们提出的激励机制能够以更低的成本吸引拥有优质训练数据的客户端参与联邦推荐，这可以使联邦推荐的经济效益提高54.9%，同时提升推荐性能。因此，我们的工作为构建联邦推荐应用的和谐健康的生态环境提供了理论和技术支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
