  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.
