  Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in
communication efficiency stemming from the considerable size of parameters and
extensive communication rounds. However, existing FKGE methods only focus on
reducing communication rounds by conducting multiple rounds of local training
in each communication round, and ignore reducing the size of parameters
transmitted within each communication round. To tackle the problem, we first
find that universal reduction in embedding precision across all entities during
compression can significantly impede convergence speed, underscoring the
importance of maintaining embedding precision. We then propose bidirectional
communication-efficient FedS based on Entity-Wise Top-K Sparsification
strategy. During upload, clients dynamically identify and upload only the Top-K
entity embeddings with the greater changes to the server. During download, the
server first performs personalized embedding aggregation for each client. It
then identifies and transmits the Top-K aggregated embeddings to each client.
Besides, an Intermittent Synchronization Mechanism is used by FedS to mitigate
negative effect of embedding inconsistency among shared entities of clients
caused by heterogeneity of Federated Knowledge Graph. Extensive experiments
across three datasets showcase that FedS significantly enhances communication
efficiency with negligible (even no) performance degradation.
