Centralized　learning　now　faces　data　mapping　and　security　constraints　that　make　it　difficult　to　carry　out.　Federated　learning　with　a　distributed　learning　architecture　has　changed　this　situation.　By　restricting　the　training　process　to　participants’　local,　federated　learning　addresses　the　model　training　needs　of　multiple　data　sources　while　better　protecting　data　privacy.　However,　in　real-world　application　scenarios,　federated　learning　faces　the　need　to　achieve　fairness　in　addition　to　privacy　protection.　In　practice,　it　could　happen　that　some　federated　learning　participants　with　specific　motives　may　short　join　the　training　process　to　obtain　the　current　global　model　with　only　limited　contribution　to　the　federated　learning　whole,　resulting　in　unfairness　to　the　participants　who　previously　participated　in　federated　learning.　We　propose　the　FedACC　framework　with　a　server-initiated　global　model　accuracy　control　method　to　address　this　issue.　Besides　measuring　the　accumulative　contributions　of　newly　joined　participants　and　providing　participants　with　a　model　with　an　accuracy　that　matches　their　contributions,　the　FedACC　still　guarantees　the　validity　of　participant　gradients　based　on　the　accuracy-decayed　model.　Under　the　FedACC　framework,　users　do　not　have　access　to　the　full　version　of　the　current　global　model　early　in　their　training　participation.　However,　they　must　produce　a　certain　amount　of　contributions　before　seeing　the　full-accuracy　model.　We　introduce　an　additional　differential　privacy　mechanism　to　protect　clients’　privacy　further.　Experiments　demonstrate　that　the　FedACC　could　obtain　about　10%　to　20%　accuracy　gain　compared　to　the　state-of-art　methods　while　balancing　the　fairness,　performance,　and　security　of　federated　learning.　IEEE