FedGReS: Federated Generative Recommendation via Global Semantic-ID Relation Sharing

Generative recommendation predicts next items by autoregressively generating their hierarchical Semantic IDs (SIDs). We study this paradigm in cross-silo federated learning, where user-item interaction sequences remain local to each client. Learning sequence patterns for next-item prediction is challenging when exclusive-user preferences vary across clients and shared users' interaction histories are fragmented across them. To address these challenges, we propose FedGReS, which complements parameter aggregation with first-level SID graphs built from pooled transition and co-occurrence counts. Distinct item pairs mapped to the same pair of first-level SID codewords contribute to shared counts, making cross-client relational evidence available to local sequence learning. The resulting graphs condition first-level SID representations of historical items through graph propagation and gated residual injection. The graph-conditioning mechanism and the generative model are jointly trained under the full-SID generation objective. FedGReS exchanges neither user identifiers nor interaction sequences. Across three Amazon Reviews 2023 datasets, FedGReS achieves the highest mean performance in all 12 dataset-metric settings, with relative gains of up to 6.98% over the strongest baseline.