Secure Federated Matrix Factorization
Secure Federated Matrix Factorization
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DOI：
10.1109/mis.2020.3014880
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发表时间：
2021-09-01
影响因子：
6.4
通讯作者：
Yang, Qiang
中科院分区：
文献类型：
--
作者：
Chai, Di;Wang, Leye;Yang, Qiang
为了保护用户隐私和满足法律法规，联合(机器)学习近年来得到了广泛的关注。联合学习的关键原理是训练机器学习模型，而不需要知道每个用户的个人原始私有数据。在本文中，我们提出了一种联邦学习环境下的安全矩阵分解框架FedMF。首先，我们设计了一个用户级的分布式矩阵分解框架，当每个用户只向服务器上传梯度信息(而不是原始偏好数据)时，模型就可以学习。虽然梯度信息似乎是安全的，但我们证明它仍然可能泄露用户的原始数据。为此，我们用同态加密增强了分布式矩阵分解框架。我们实现了FedMF的原型，并用真实的电影评分数据集对其进行了测试。结果验证了FedMF的可行性。我们还讨论了在实践中应用FedMF的挑战，为未来的研究。
To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data. In this article, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users' raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.