Abstract: A computer-implemented method for identifying and rectifying a machine learning drift in a federated learning deployment comprising a parameter server and a plurality of worker nodes, wherein a first worker node comprises: a first machine learning model trained using a first data source; and a second machine learning model trained using a second data source; wherein the first data source is generated by the first worker node and the second data source is generated by a second worker node; the method comprising calculating, by the first worker node, using a trusted data set, a first performance metric associated with the first machine learning model and a second performance metric associated with the second machine learning model and determining, by the first worker node, whether a potential drift has occurred in at least one of the first and the second machine learning models.