FED-GAME: PERSONALIZED FEDERATED LEARNING WITH GRAPH ATTENTION MIXTURE-OF-EXPERTS FOR TIME-SERIES FORECASTING
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We propose Fed-GAME, a framework that models personalized aggregation as message passing over a learnable dynamic implicit…