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：推荐系统在过去数年间已变得无处不在。它们解决了众多用户面临的选择困境问题，并被众多在线企业用于提升用户参与度和销售额。除却诸如在社交网络中制造信息茧房等批评之外，推荐系统常因收集大量个人数据而备受指责。然而，要实现个性化推荐，本质上仍需获取个人信息。近期提出的名为联邦学习的分布式学习方案，使得在不集中收集个人用户数据的情况下进行学习成为可能。据此，我们提出了一种电影推荐系统，该系统能够在多个层面提供隐私保护及可信性：首先且首要的是，该系统采用联邦学习进行训练，因此天然具备隐私保护特性，同时仍能使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，该方案不仅解决了数据非独立同分布及本地数据集规模较小的问题，还通过早期聚合客户端更新来防范输入数据重构攻击。最后，为降低通信开销，我们应用了压缩技术，该技术能将所交换的神经网络参数集显著压缩至原始体量的极小部分。我们推测，这种有损量化阶段或许也能进一步提升数据隐私性。