Abstract: A method for ranking includes the steps of receiving, at least one model from a central server to form at least one model node; training, the at least one model node with at least one data node to generate a trained model; generating, a weight from each of the at least one data node for the trained model; transferring, the weight from the at least one data node to the central server; inferencing, an inference output using the trained model and the data node; determining, an edge between the at least one data node and the model node, wherein the edge is determined depending on the influence of the data node or the model node on each other in generating the trained model and the inference output; determining a score for the data node and the model node based on the edge formed.

Abstract: A method for ranking includes the steps of receiving, at least one model from a central server to form at least one model node; training, the at least one model node with at least one data node to generate a trained model; generating, a weight from each of the at least one data node for the trained model; transferring, the weight from the at least one data node to the central server; inferencing, an inference output using the trained model and the data node; determining, an edge between the at least one data node and the model node, wherein the edge is determined depending on the influence of the data node or the model node on each other in generating the trained model and the inference output; determining a score for the data node and the model node based on the edge formed.