Federated　learning　(FL)　enables　collaborative　training　of　machine　learning　models　across　distributed　medical　data　sources　without　compromising　privacy.　However,　applying　FL　to　medical　image　analysis　presents　challenges　like　high　communication　overhead　and　data　heterogeneity.　This　paper　proposes　novel　FL　techniques　using　explainable　artificial　intelligence　(XAI)　for　efficient,　accurate,　and　trustworthy　analysis.　A　heterogeneity-aware　causal　learning　approach　selectively　sparsifies　model　weights　based　on　their　causal　contributions,　significantly　reducing　communication　requirements　while　retaining　performance　and　improving　interpretability.　Furthermore,　blockchain　provides　decentralized　quality　assessment　of　client　datasets.　The　assessment　scores　adjust　aggregation　weights　so　higher-quality　data　has　more　influence　during　training,　improving　model　generalization.　Comprehensive　experiments　show　our　XAI-integrated　FL　framework　enhances　efficiency,　accuracy　and　interpretability.　The　causal　learning　method　decreases　communication　overhead　while　maintaining　segmentation　accuracy.　The　blockchain-based　data　valuation　mitigates　issues　from　low-quality　local　datasets.　Our　framework　provides　essential　model　explanations　and　trust　mechanisms,　making　FL　viable　for　clinical　adoption　in　medical　image　analysis.　IEEE