Abstract: One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.

Abstract: A method, computer system, and computer program product are provided. A set of classifiers are applied to metric definitions of an electronic contract between computing devices of a service provider and a service consumer. Each classifier includes a selector pattern and a set of variable declarations. Performance data of the service provider computing device are filtered according to the selector pattern of the each classifier. One or more algebraic expressions of the metric definitions are evaluated in accordance with the each classifier and the filtered performance data to determine compliance of the service provider computing device with the electronic contract. Evaluation results indicating whether the service provider computing device is in compliance with the electronic contract are captured and reported.

Abstract: One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.

Abstract: A method, computer system, and computer program product are provided. A set of classifiers are applied to metric definitions of an electronic contract between computing devices of a service provider and a service consumer. Each classifier includes a selector pattern and a set of variable declarations. Performance data of the service provider computing device are filtered according to the selector pattern of the each classifier. One or more algebraic expressions of the metric definitions are evaluated in accordance with the each classifier and the filtered performance data to determine compliance of the service provider computing device with the electronic contract. Evaluation results indicating whether the service provider computing device is in compliance with the electronic contract are captured and reported.