With　the　advent　of　the　Internet　of　things　(IoT)　era,　federated　learning　plays　an　important　role　in　breaking　through　traditional　data　barriers　and　effectively　realizing　data　privacy　and　security　in　the　process　of　sharing.　However,　the　demand　of　the　practical　problems　makes　the　algorithm　still　have　great　challenges　in　effectively　balancing　various　factors,　such　as　privacy　security,　accuracy,　computing　efficiency　and　so　on.　To　challenge　this　problem,　a　two-stage　federated　optimization　algorithm　based　on　robust　and　multitasking　learning　is　designed.　In　optimization　client　local　model　stage,　an　adaptive　weight　assignment　mechanism　is　adopted　to　guide　robust　learning　based　on　multiple　untrusted　sources　data,　which　aims　to　ensure　the　credibility　and　robustness　of　the　client　model　and　obtain　a　reliable　client　model.　To　address　the　information　leakage　problem　during　the　server–client　global　model　optimization　stage,　a　privacy　patch　layer　is　added　to　the　client　local　model　in　multitask　learning　and　maintain　its　privacy　parameters　stored　on　the　client　model　during　the　global　model　parameter　aggregation　process,　which　aims　to　meet　the　personalized　requirements　and　performance　requirements　of　protecting　the　model　privacy.　To　effectively　measure　the　performance　of　our　algorithm,　two　extensive　experiments　are　carried　out　to　verify　the　robustness　and　accuracy　of　model　under　different　datasets,　respectively.　In　addition,　simulation　results　show　that　our　algorithm　successfully　suppresses　the　impact　of　corrupted　or　irrelevant　sources　on　performance,　and　its　performance　is　better　than　the　other　two　robust　distributed　learning　baseline　methods　in　the　client　local　model　optimization　stage.　At　the　same　time,　our　algorithm　achieves　better　accuracy　performance　than　other　advanced　personalized　optimization　algorithms　in　the　server–client　global　model　optimization　stage.　Finally,　it　achieves　a　good　balance　between　robustness,　computational　efficiency　and　model　privacy　protection.　©　2023　Elsevier　B.V.