Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting

Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Existing personalized federated learning (PFL) methods ignore the intrinsic geometric properties of diverse graph structures. We propose FlatLand, a novel personalized federated learning method that embeds different clients' data in tailored Lorentz space of hyperbolic geometry. Our key insight is that hyperbolic geometry naturally accommodates the intrinsic negative curvature prevalent in real-world graphs, while the time-like dimension in Lorentz space provides a principled way to encode client-specific heterogeneity. We develop a parameter decoupling strategy that separates heterogeneous information (captured in time-like parameters) from common knowledge (preserved in space-like parameters), enabling direct aggregation without requiring client similarity estimation and extra calculation modules. Empirical results on diverse federated graph learning tasks demonstrate that FlatLand achieves superior performance, particularly in low-dimensional settings.

Federated graph learning (FGL) is a natural paradigm for social-media user graphs, where language communities, regional markets, and service boundaries can prevent raw graph pooling. We use the Twitch Gamers networks as the primary live-streaming social-media benchmark, and study a question that is often hidden by representation-level evaluation: after local message passing, what update signals are actually exposed to server aggregation? Through update-space measurements, we identify an aggregation-level failure in which graph-domain clients gradually place salient update signals on less shared parameter coordinates, while message-passing backbones show weaker cross-domain directional compatibility than an MLP control. This update-support fragmentation means that standard averaging can dilute locally important coordinates even when no raw graph data are exchanged. Across five backbones, homophily remains relevant but is not the dominant correlate of support retention; feature, label, and degree discrepancies show stronger associations. These findings indicate that graph-domain shifts damage not only local representations, but also the coordinate support on which aggregation operates. Motivated by this diagnosis, we propose FedIA, a plug-and-play server-side importance-aware aggregation method. Importance Masking selects a shared high-magnitude coordinate support, and Contribution-Aware Momentum Weighting smooths client contributions within that support. FedIA requires no raw graph sharing, no graph-statistics upload, and no auxiliary communication payload, while adding only $O(D+N)$ persistent server state for $D$ model coordinates and $N$ clients.

Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence modeling and collaborative training. We propose F$^2$STNet, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA). The spectral branch exposes graph-frequency structure, while the state-space layer models long temporal dependencies with linear complexity in the sequence length. FFA adjusts the FedAvg prior using client validation losses and an increasing fairness schedule. Experiments on PeMS04, HZMetro, and KnowAir show favorable forecasting accuracy relative to the evaluated baselines; federated experiments on PeMS04 additionally improve worst-client and client-dispersion metrics.