  Graph neural networks (GNNs) have gained wide popularity in recommender
systems due to their capability to capture higher-order structure information
among the nodes of users and items. However, these methods need to collect
personal interaction data between a user and the corresponding items and then
model them in a central server, which would break the privacy laws such as
GDPR. So far, no existing work can construct a global graph without leaking
each user's private interaction data (i.e., his or her subgraph). In this
paper, we are the first to design a novel lossless federated recommendation
framework based on GNN, which achieves full-graph training with complete
high-order structure information, enabling the training process to be
equivalent to the corresponding un-federated counterpart. In addition, we use
LightGCN to instantiate an example of our framework and show its equivalence.
