Multimodal　information　plays　an　important　role　in　the　advanced　Internet　of　Things　(IoT)　in　the　era　of　6G,　which　provides　reliable　and　comprehensive　assistance　for　downstream　tasks　through　further　fusion　and　analysis　via　federated　learning　(FL).　One　of　the　primary　challenges　in　FL　is　data　heterogeneity,　which　may　lead　to　domain　shifts　and　sharply　different　local　long-tailed　category　distribution　across　nodes.　These　issues　hinder　the　large-scale　deployment　of　FL　in　IoT　applications　equipped　with　multiple　various　multimodal　sensors　due　to　performance　deterioration.　In　this　paper,　we　propose　a　novel　multimodal　fusion　framework　to　tackle　the　aforementioned　coupled　problems　arising　during　the　cooperative　fusion　of　multimodal　information　without　privacy　exposure　among　decentralized　nodes　equipped　with　diverse　sensors.　Specifically,　we　introduce　a　flexible　global　logit　alignment　(GLA)　method　based　on　multi-view　domains.　This　method　enables　the　fusion　of　diverse　multimodal　information　with　the　consideration　of　domain　shifts　caused　by　modality-based　data　heterogeneity.　Furthermore,　we　propose　a　novel　local　angular　margin　(LAM)　scheme,　which　dynamically　adjusts　decision　boundaries　for　locally　seen　categories　while　preserving　global　decision　boundaries　for　unseen　categories.　This　effectively　mitigates　severe　model　divergence　caused　by　significantly　different　category　distributions.　Extensive　simulations　demonstrate　the　superiority　of　the　proposed　framework,　which　exhibits　significant　merits　in　tackling　model　degeneration　caused　by　data　heterogeneity　and　enhancing　modality-based　generalization　for　heterogeneous　scenarios.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.