Abstract: The present disclosure provides a federated learning based identification of non-malicious classification models where the conventional model fails to perform. Initially, the system receives a local classification model from each of a plurality of clients. Further, a set of one-dimensional arrays are obtained based on a plurality of local classification models associated with the plurality of clients using a flattening technique. Further, a major cluster and a minor cluster are obtained by clustering the set of one-dimensional arrays using a clustering technique. After clustering, a plurality of active classification models are selected based on the major cluster and the minor cluster using an epsilon cluster selection technique. Further, a global classification model is selected from the plurality of active models using a random selection technique. Finally, the selected global classification model is transmitted to each of the plurality of clients.