The　advent　of　big　data　and　AI　has　resulted　in　a　data　revolution　across　many　sectors,　healthcare　among　them.　However,　the　private　nature　of　patient　information　frequently　prevents　its　sharing,　resulting　in　data　storage　that　slow　the　development　of　AI　in　healthcare.　Federated　learning　(FL)　emerges　as　a　possible　answer　to　these　problems.　As　a　result　of　FL,　reli-able　illness　detection　models　can　be　developed　on　distributed　data　held　by　diverse　individuals　without　compromising　data　privacy.　In　this　study,　we　investigate　how　FL　may　help　solve　problems　associated　with　data　sharing　and　privacy　in　biomed-ical　health.　This　research　seeks　to　contribute　to　the　incorporation　of　artificial　intelligence　in　biological　health　by　reviewing　relevant　literature　and　case　studies　to　provide　a　full　know　l-edge　of　FL＇s　benefits　and　limitations　in　disease　identification　and　diagnosis.　©　2023　IEEE.