In　federated　learning　(FL),　the　distribution　of　data　in　clients　is　always　heterogeneous,　which　makes　the　unified　model　trained　in　FL　unable　to　meet　the　demand　of　each　client.　To　combat　this　issue,　a　personalized　federated　learning　algorithm　with　meta　learning　and　knowledge　distillation　is　proposed,　in　which　the　knowledge　distillation　and　meta-learning　with　FL　and　incorporating　the　personalization　are　combined　into　the　training　of　FL.　In　each　global　iteration,　the　global　model　(teacher　model)　update　itself　according　to　the　feedback　from　the　local　model　(student　model)　during　the　knowledge　distillation.　Therefore,　each　client　can　obtain　a　better　personalized　model.　Simulation　results　show　that　compared　with　the　existing　personalized　algorithms,　the　proposed　algorithm　can　achieve　a　better　compromise　between　global　accuracy　and　personalization　accuracy　while　improving　the　personalization　accuracy.　©　2023　Beijing　University　of　Posts　and　Telecommunications.　All　rights　reserved.