The application discloses a kind of federal
learning methods based on
high availability non-interactive security aggregation scheme, its characteristics are based on the security aggregation method of
pairing mask, through asynchronous public subset
consensus algorithm, it is realized on distributed
server cluster non-interactive
high availability security aggregation federal learning, specifically includes: (A) initialization stage (B) aggregation stage epoch 0, first round, user;(C) aggregation stage epoch 0, first round,
server;(D) aggregation stage epoch 0, second round, user;(E) aggregation stage epoch 0, second round,
server;(F) aggregation stage epoch k (k>0), user and (G) aggregation stage epoch k (k>0), server etc.Steps.The application has
high availability security aggregation, small amount of calculation, resists malicious
adversary and other advantages compared with prior art, realizes the
privacy protection of gradient information, and guarantees the
correctness of federal learning training result, is established in the
asynchronous network model closest to real
network model, with higher practical application value.