Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are employed by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Accordingly, we present a complete recommender system for movie recommendations, which provides privacy and thus trustworthiness on two levels: First, it is trained using federated learning and thus is, by its very nature, privacy-preserving, while still enabling individual users to benefit from global insights. And second, a novel federated learning scheme, FedQ, is employed, which not only addresses the problem of non-i.i.d. and small local datasets, but also prevents input data reconstruction attacks by aggregating client models early. To reduce the communication overhead, compression is applied, which significantly reduces the exchanged neural network updates to a fraction of their original data. We conjecture that it may also improve data privacy through its lossy quantization stage.

翻译：推荐系统在过去几年中已变得无处不在。它们解决了众多用户面临的选择困难问题，并被许多在线企业用于提升用户参与度和销售额。除了诸如在社交网络中制造信息茧房等批评外，推荐系统常因收集大量个人数据而受到指责。然而，要实现个性化推荐，个人数据是根本需求。一种名为联邦学习的新型分布式学习方案，使得无需集中收集用户数据即可从个人数据中学习。据此，我们提出了一套完整的电影推荐系统，该系统在以下两个层面提供隐私保护，从而建立可信度：第一，系统采用联邦学习进行训练，因此天生具有隐私保护特性，同时仍能使个体用户受益于全局洞察。第二，采用了一种新颖的联邦学习方案FedQ，它不仅解决了非独立同分布和小型本地数据集的问题，还通过早期聚合客户端模型来防止输入数据重建攻击。为降低通信开销，应用了压缩技术，将交换的神经网络更新量显著缩减至原始数据的极小部分。我们推测，其有损量化阶段也可能进一步提升数据隐私。