Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized 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. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.

翻译：推荐系统在过去几年中变得无处不在。它们解决了许多用户面临的选择过载问题，并被众多在线企业用于提升用户参与度和销售额。除创建社交网络信息茧房等批评外，推荐系统常因收集大量个人数据而受到指责。然而，实现个性化推荐本质上需要个人信息。最近一种名为联邦学习的分布式学习方案使得无需集中收集数据即可从用户个人数据中学习。为此，我们提出一种面向电影推荐的推荐系统，该系统在多层级实现隐私保护与可信赖性：首先且最重要的是，它采用联邦学习进行训练，因此本质上是隐私保护的，同时仍能让用户受益于全局洞察。此外，我们还采用了一种名为FedQ的新型联邦学习方案，该方案不仅解决了数据非独立同分布和小规模本地数据集的问题，还通过提前聚合客户端更新来防止输入数据重构攻击。最后为降低通信开销，我们应用了压缩技术，该技术将交换的神经网络参数集显著压缩至原始规模的极小比例。我们推测，其有损量化阶段也可能提升数据隐私保护效果。