A federated training system comprises a plurality of models, a plurality of training datasets, and a runtime intermediary. Models in the plurality of models have model coefficients responsive to training. Training datasets in the plurality of training datasets are annotated with ground truth labels to train the models. The training datasets are accompanied with training provisioning parameters and privacy parameters. The runtime intermediary is interposed between the models and the training datasets, and configured to receive requests for training the models on the training datasets, the requests accompanied with training acquisition parameters, to respond to the requests by matching the models with the training datasets based on evaluating the training acquisition parameters against the training provisioning parameters, to train the models on the matched training datasets in accordance with the privacy parameters to generate gradients with respect to the model coefficients.
Tensor Exchange for Federated Cloud Learning