Abstract: A method includes assembling an explainable artificial intelligence committee comprising two or more explainable artificial intelligence techniques, performing the explainable artificial intelligence techniques on results generated by a machine learning model, as a result of the performing, obtaining respective explanations, generated by each of the explainable artificial intelligence techniques, for the results generated by the machine learning model, and determining that one of the explanations was compromised by an attacker.

Abstract: One method includes stochastically selecting, by a central node, a subset of edge nodes from a group of edge nodes that collectively defines a federation, querying, by the central node, the edge nodes of the subset for updates to a global model maintained by the central node, receiving, by the central node from the edge nodes of the subset, respective updates to one or more layers of the global model, and updating, by the central node, the global model, using the updates received from the edge nodes of the subset.

Abstract: Techniques are provided for evaluation of machine learning models using agreement scores. One method comprises obtaining two or more of: (i) a first set of quantitative features characterizing model parameters of a machine learning model; (ii) a second set of quantitative features characterizing a training process used to train the machine learning model; and (iii) a third set of quantitative features characterizing a training dataset used to train the machine learning model; generating a score based on an aggregation of at least portions of the two or more of the first set, the second set and the third set, wherein the score is based on an agreement of the machine learning with designated characteristics; and initiating an automated action based on the score. The automated action may comprise updating the machine learning model; generating a notification in connection with an audit; and/or selecting a machine learning model for deployment.

Abstract: One example method includes initiating an audit of a machine learning model, providing input data to the machine learning model as part of the audit, while the audit is running, receiving information regarding operation of the machine learning model, wherein the information comprises a computational resource footprint, analyzing the computational resource footprint, and determining, based on the analyzing, that the computational resource footprint is characteristic of an adversarial attack on the machine learning model.