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，并被大量在线企业用于提升用户参与度和销售额。除了制造社交网络中的信息茧房等批评外，推荐系统常因大量收集个人数据而受到指责。然而，要实现个性化推荐，个人信息的收集本质上不可或缺。最近一种名为联邦学习的分布式学习方案，使得无需集中收集个人用户数据即可进行学习。为此，我们提出了一种电影推荐系统，它在多个层面提供隐私保护，从而增强可信度：首先且最重要的是，该系统采用联邦学习进行训练，这一特性使其天然具备隐私保护能力，同时仍能让用户受益于全局洞察。其次，我们采用了一种名为FedQ的新型联邦学习方案，该方案不仅解决了非独立同分布性和本地数据集规模小的问题，还通过提前聚合客户端更新来防止输入数据重建攻击。最后，为降低通信开销，我们应用了压缩技术，将交换的神经网络参数显著压缩至原始大小的一小部分。我们推测，这种有损量化阶段可能进一步改善数据隐私。