Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.

Abstract: The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Abstract: The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Abstract: The technology disclosed relates to a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint.

Abstract: The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Abstract: The technology disclosed provides systems and methods related to preventing exfiltration of training data by feature reconstruction attacks on model instances trained on the training data during a training job. The system comprises a privacy interface that presents a plurality of modulators for a plurality of training parameters. The modulators are configured to respond to selection commands via the privacy interface to trigger procedural calls. The procedural calls modify corresponding training parameters in the plurality of training parameters for respective training cycles in the training job. The system comprises a trainer configured to execute the training cycles in dependence on the modified training parameters. The trainer can determine a performance accuracy of the model instances for each of the executed training cycles.

Abstract: The technology disclosed relates to a system and method of conducting virtual clinical trials. The system comprises a sponsor server configured to specify a target mapping of a clinical trial objective mapper. The target mapping maps participant-specific clinical data to an objective of a virtual clinical trial. The system comprises a plurality of edge devices accessible by respective participants in a plurality of participants. The system comprises a clinical trial conductor server configured to distribute coefficients of the clinical trial objective mapper to respective edge devices to implement distributed training of the clinical trial objective mapper. The clinical trial conductor server is configured to receive participant-specific gradients generated during the distributed training in response to processing participant-specific clinical data.

Abstract: The technology disclosed relates to a system and method for training processing engines. A processing engine can have at least a first processing module and a second processing module. The first processing module in each processing engine is different from a corresponding first processing module in every other processing engine. The second processing module in each processing engine is same as a corresponding second processing module in every other processing engine. The system can include a deployer that deploys each processing engine to a respective hardware module for training. The system can comprise a forward propagator which during forward pass stage can process inputs through first processing modules and produce an intermediate output for each first processing module. The system can comprise a backward propagator which during backward pass stage can determine gradients for each second processing module on corresponding final outputs and ground truths.

Abstract: 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.