Data　heterogeneity　caused　by　Non-Independent　and　Identically　Distributed　(non-IID)　data　in　local　clients　imposes　limitations　and　challenges　on　the　training　and　performance　of　Federated　Learning.　Current　researches　on　this　issue　mainly　focus　on　optimizing　a　single　global　model　or　developing　personalized　model　for　each　client,　neglecting　the　essential　client　relationships.　However,　despite　the　heterogeneity　of　client　data,　common　characteristics　that　can　be　leveraged　still　exist.　Therefore,　We　proposed　a　novel　personalized　federated　learning　approach,　called　pFedBEA.　The　method　decomposes　the　client　model　into　a　body　model　(extractor)　and　a　head　model　(classifier)　to　respectively　adapt　to　common　features　and　personalized　attributes.　Subsequently,　periodically　abandoning　the　global　server　aggregation　and　exchanging　the　body　models　among　different　clients,　which　enables　local　personalization　while　learning　from　multiple　data　sources,　promoting　knowledge　sharing　and　enhancing　the　generalization　ability　of　the　global　model.　pFedBEA　not　only　improves　model　performance　but　also　reduces　the　number　of　aggregation　rounds　and　communication　time,　achieving　overall　efficiency　optimization.　We　conducted　extensive　experiments　on　a　range　of　datasets,　demonstrating　that　pFedBEA　achieves　higher　accuracy,　superior　aggregation　efficiency　and　communication　efficiency.　©　2024　IEEE.