Because　of　the　effectiveness　of　federated　learning　in　protecting　privacy　and　breaking　the　＂data　silo＂　phenomenon,　it　has　been　widely　studied　and　applied　in　recent　years.　Firstly,　the　definition,　classification　and　structure　of　federated　learning　are　expounded.　Secondly,　the　principle　and　development　of　federated　algorithm　are　summarized.　Then,　it　analyzes　the　challenges　faced　by　federated　learning,　such　as　privacy　protection,　security　aggregation,　heterogeneity,　and　traffic　cost,　and　summarizes　the　status　quo　of　federated　optimization　algorithm　from　these　aspects.　Finally,　it　analyzes　the　development　and　feasibility　of　federated　learning　through　the　application　of　various　industries,　and　summarizes　the　prospect.　©　2023　Copyright　held　by　the　owner/author(s)