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

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.