FedTIP: Communication-Efficient Federated Temporal Prompting for Few-Shot Dynamic Graph Adaptation

Abstract

Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonly exchange larger trainable states. This paper presents FedTIP, a prompt-level federated method for client-local few-shot adaptation of temporal interaction graphs. A centralized historical stage constructs a temporal knowledge bank whose encoder is frozen during downstream federation. Clients optimize interaction-conditioned prompts on local supervised events and transmit a compact prompt-side message. Prototype anchoring and a reliability rule based on support coverage and update magnitude combine heterogeneous client updates, while prompt-side proximal regularization and stage-scoped negative sampling preserve the temporal protocol. Experiments on Wikipedia, Reddit, and MOOC cover temporal node classification and transductive and inductive link prediction. FedTIP records the highest mean AUC-ROC in the nine reported task–dataset cells, with gains of 1.83–14.69 points over the strongest federated baseline in each cell. Its measured cumulative float32 uplink is 0.40 MiB per downstream task, 64.6–99.6% below the evaluated baselines.