Motivated　by　the　modern　phenomenon　of　distributed　data　collected　by　edge　devices　at　scale,　federated　learning　can　use　large　amounts　of　training　data　from　diverse　users　for　better　representation　and　generalization.　To　improve　flexibility　and　scalability,　a　new　federated　optimization　algorithm,　named　multi-index　federated　aggregation　algorithm　based　on　trusted　verification　(TVFedmul)　is　proposed.　It　overcomes　a　series　of　problems　caused　by　the　original　aggregation　algorithm,　which　only　takes　the　single　index　of　data　quantity　as　a　reference　factor　to　measure　the　aggregation　weight　of　each　client.　The　improved　algorithm　is　based　on　multi-index　measurement.　It　reflects　the　comprehensive　ability　of　clients　more　thoroughly,　to　make　overall　judgments.　Further,　to　achieve　customized　federated　learning,　a　hyperparameter　alpha　is　introduced.　It　can　be　changed　to　determine　the　importance　of　indexes.　Finally,　via　extensive　experimentation,　it　has　been　observed　that　the　improved　algorithm　is　faster,　and　the　accuracy　reaches　94.59%,　which　is　2.53%　higher　than　that　of　FedAvg　(92.06%).