Federated　learning　is　a　multi-party　distributed　machine　learning　system　that　allows　each　participant　to　complete　the　training　task　without　their　data　out　of　the　locality.　The　real　meaning　of　the　realization　of　data　availability　is　invisible　to　protect　personal　privacy　and　data　security.　Although　some　institutions　need　to　obtain　their　data＇s　benefit　by　sharing　their　own　data　sets,　the　potential　trust　risks　lead　to　the　data　not　being　better　utilized　cooperatively.　In　addition,regulatory　requirements　on　data　computing　and　sharing　are　different　in　various　countries.　These　contribute　to　enormous　challenges　in　the　actual　application　of　federated　learning.　In　this　research,　we　make　a　comprehensive　review　of　the　application　patterns　of　federated　learning　in　different　fields.　Then,　we　illustrate　the　social　responsibilities　of　federated　learning　from　four　dimensions,　including　compliance　application,　system　security　mechanism,　the　trust　mechanism,　and　ethical　security.　Finally,　based　on　the　current　characteristics　and　regulatory　requirements　of　federated　learning,　we　discuss　the　future　research　directions　for　federated　learning.