In　recent　years,　healthcare　data　from　various　sources　such　as　clinical　institutions,　patients,　and　pharmaceutical　industries　have　become　increasingly　abundant.　However,　due　to　the　complex　healthcare　system　and　data　privacy　concerns,　aggregating　and　utilizing　these　data　in　a　centralized　manner　can　be　challenging.　Federated　learning　(FL)　has　emerged　as　a　promising　solution　for　distributed　training　in　edge　computing　scenarios,　utilizing　on-device　user　data　while　reducing　server　costs.　In　traditional　FL,　a　central　server　trains　a　global　model　sampled　client　data　randomly,　and　the　server　combines　the　collected　model　from　different　clients　into　one　global　model.　However,　for　not　independent　and　identically　distributed　(non-i.i.d.)　datasets,　randomly　selecting　users　to　train　server　is　not　an　optimal　choice　and　can　lead　to　poor　model　training　performance.　To　address　this　limitation,　we　propose　the　Federated　Multi-Center　Clustering　algorithm　(FedMCC)　to　enhance　the　robustness　and　accuracy　for　all　clients.　FedMCC　leverages　the　Model-Agnostic　Meta-Learning　(MAML)　algorithm,　focusing　on　training　a　robust　base　model　during　the　initial　training　phase　and　better　capturing　features　from　different　users.　Subsequently,　clustering　methods　are　used　to　ensure　that　features　among　users　within　each　cluster　are　similar,　approximating　an　i.i.d.　training　process　in　each　round,　resulting　in　more　effective　training　of　the　global　model.　We　validate　the　effectiveness　and　generalizability　of　FedMCC　through　extensive　experiments　on　public　healthcare　datasets.　The　results　demonstrate　that　FedMCC　achieves　improved　performance　and　accuracy　for　all　clients　while　maintaining　data　privacy　and　security,　showcasing　its　potential　for　various　healthcare　applications.　©　2023　Elsevier　Inc.