Remote　healthcare　has　been　an　important　application　of　6G　and　is　increasingly　attracting　attention.　In　the　field　of　medical　image　analysis,　federated　learning　(FL)　is　widely　considered　for　medical　data　collected　by　Internet　of　medical　things　(loMT)　devices　from　different　hospitals.　In　FL,　communication　overhead　and　local　data　valuation　are　two　inevitable　issues　that　urgently　need　to　be　addressed.　The　communication　overhead　between　local　clients　and　a　central　server　can　assist　in　enhancing　the　perception　ability　of　FL.　The　data　quality　of　local　clients　should　be　positively　correlated　with　the　contribution　of　their　aggregation　model.　As　such,　this　article　discusses　an　efficient　causal　learning-based　compression　scheme　of　local　data　to　reduce　the　amount　of　data　for　communication,　namely,　feature-heterogeneity-aware　model,　which　will　lower　communication　overhead　without　affecting　the　performance.　Additionally,　local　client　evaluation　based　on　a　model-driven　and　artificial　intelligence　(AI)-driven　mode　is　analyzed　respectively　with　the　assistance　of　blockchain　due　to　the　data　sensitivity　of　medical　images　in　this　article.　As　a　result,　the　client　weight　during　global　aggregation　can　be　adjusted　adaptively　according　to　their　objective　contribution.　The　simulations　are　made,　and　the　results　validate　the　effectiveness　of　proposed　schemes.