Multimodal　learning　mines　and　analyzes　multimodal　data　in　reality　to　better　understand　and　appreciate　the　world　around　people.　However,　how　to　exploit　this　rich　multimodal　data　without　violating　user　privacy　is　a　key　issue.　Federated　learning　is　a　privacy-conscious　alternative　to　centralized　machine　learning,　therefore　many　researchers　have　combined　federated　learning　with　multimodal　learning　to　break　down　data　barriers　for　the　purpose　of　jointly　leveraging　multiple　modal　data　from　different　clients　for　modeling.　In　order　to　provide　a　systematic　summarize　of　multimodal　federated　learning,　this　paper　describes　the　basic　mode　of　multimodal　federated　learning,　multimodal　fusion　based　on　federated　learning,　multimodal　federated　learning　optimization　and　multimodal　federated　learning　application,　and　introduces　each　type　of　multimodal　federated　learning　methods　in　detail.　Finally,　the　future　research　trends　of　multimodal　federated　learning　are　discussed　and　analyzed,　mainly　including　the　optimization　of　multimodal　federated　learning,　privacy-　preserving　techniques　for　multimodal　federated　learning,　multimodal　federated　few-shot　learning　&　multimodal　federated　semi-supervised　learning,　and　data　and　knowledge-driven　multimodal　federated　learning.