News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.

翻译：新闻推荐对于个性化新闻访问至关重要。现有大多数新闻推荐方法依赖于集中存储用户历史新闻点击行为数据，这可能引发隐私问题和风险。联邦学习是一种隐私保护框架，允许多个客户端在不共享私有数据的情况下协同训练模型。然而，直接以联邦方式学习许多现有新闻推荐模型所产生的计算和通信开销对用户客户端而言难以承受。本文提出一种面向隐私保护新闻推荐的高效联邦学习框架。不同于训练和通信整个模型，我们将新闻推荐模型分解为在服务器端维护的大型新闻模型和在服务器与客户端共享的轻量级用户模型，其中新闻表示和用户模型在服务器与客户端之间进行通信。具体而言，客户端从服务器请求用户模型和新闻表示，并将其本地计算的梯度发送至服务器进行聚合。服务器使用聚合后的梯度更新其全局用户模型，并进一步更新新闻模型以推断更新的新闻表示。由于本地梯度可能包含隐私信息，本文提出一种安全聚合方法，以隐私保护方式聚合梯度。在两个真实数据集上的实验表明，该方法能够在保证模型性能的前提下降低客户端的计算和通信开销。