Explainable federated learning robust to malicious clients and to non-independent and identically distributed data

A method comprising:

• receiving, at a central node, relative importances for a plurality of features input into a machine learning (ML) model usable at an edge node, thereby defining a plurality of feature importances, the central node being configured to communicate with the edge nodes;
• using, at the central node, an ML algorithm to classify the edge nodes into a number ‘k’ of node groups based on the feature importances;
• and
• for each node group among the ‘k’ node groups:
• generating, at the central node, an ML shared model using the feature importances associated with a selected subset of nodes in the node group;
• and
• deploying, at the central node, the shared model to each edge node in the node group.

A non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

• receiving, at a central node, relative importances for a plurality of features input into a machine learning (ML) model usable at an edge node, thereby defining a plurality of feature importances, the central node being configured to communicate with the edge nodes;
• using, at the central node, an ML algorithm to classify the edge nodes into a number ‘k’ of node groups based on the feature importances;
• and
• for each node group among the ‘k’ node groups:
• generating, at the central node, an ML shared model using the feature importances associated with a selected subset of nodes in the node group;
• and
• deploying, at the central node, the shared model to each edge node in the node group.