A cooperative learning system adapted to perform process monitoring in a plurality of processes arranged in chronological order at predetermined transmission time intervals, a monitoring model being set for each of the plurality of processes configured to determine whether the state of the corresponding process is normal or abnormal, the monitoring data of each process being input to the corresponding monitoring model to output a determination result of each process, the cooperative learning system comprising: a storage section configured to store, in chronological order, monitoring data and determination results of each process; and a model learning section configured to designate an origin process (n) from the plurality of processes, and perform a first learning process of a first monitoring model (Y-1) corresponding to a first process (n-1) upstream against the origin process and a second learning process of a second monitoring model (Y+1) corresponding to a second process (n+1) downstream against the origin process, wherein the first learning process is configured to associate a first determination result of the origin process in which monitoring data at a first time is input to the monitoring model (Yn) of the origin process (n) being determined to be abnormal with the monitoring data collected in the first process (n-1) at a second time that is earlier than, the first time by a transition period to generate a first learning data, and performs the learning process of the first monitoring model (Y-1) using the first learning data, the second learning process configured to associate a second determination result of the origin process in which monitoring data at a third time is input to the monitoring model (Yn) of the origin process (n) being determined to be abnormal with the monitoring data collected in the second process (n+1) at a fourth time passed from the third time by a transition period to generate a second learning data, and performs the learning process of the second monitoring model (Y+1) using the second learning data, and wherein the first monitoring model (Y-1) of the first process upstream with respect to the origin process and the second monitoring model (Y+1) of the second process downstream with respect to the origin process are configured to include a predictor sensing model that predicts abnormal occurring in the origin process based on monitoring data obtained from their own processes. a storage section configured to store, in chronological order, monitoring data and determination results of each process; and a model learning section configured to designate an origin process (n) from the plurality of processes, and perform a first learning process of a first monitoring model (Y-1) corresponding to a first process (n-1) upstream against the origin process and a second learning process of a second monitoring model (Y+1) corresponding to a second process (n+1) downstream against the origin process, wherein the first learning process is configured to associate a first determination result of the origin process in which monitoring data at a first time is input to the monitoring model (Yn) of the origin process (n) being determined to be abnormal with the monitoring data collected in the first process (n-1) at a second time that is earlier than, the first time by a transition period to generate a first learning data, and performs the learning process of the first monitoring model (Y-1) using the first learning data, the second learning process configured to associate a second determination result of the origin process in which monitoring data at a third time is input to the monitoring model (Yn) of the origin process (n) being determined to be abnormal with the monitoring data collected in the second process (n+1) at a fourth time passed from the third time by a transition period to generate a second learning data, and performs the learning process of the second monitoring model (Y+1) using the second learning data, and wherein the first monitoring model (Y-1) of the first process upstream with respect to the origin process and the second monitoring model (Y+1) of the second process downstream with respect to the origin process are configured to include a predictor sensing model that predicts abnormal occurring in the origin process based on monitoring data obtained from their own processes.