Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to train a machine learning model. An example apparatus to generate adaptive hyper-parameters includes a model aggregator to, in response to obtaining at least one model trained using a first set of hyper-parameters of a probability distribution, generate a loss reduction, a hyper-parameter generator to, when the loss reduction satisfies a loss threshold, update the probability distribution and generate a second set of hyper-parameters using the updated probability distribution, and an interface to transmit the second set of hyper-parameters to a client.

Abstract: Systems, methods, apparatus, and articles of manufacture to prevent unauthorized release of information associated with a function as a service are disclosed. A system disclosed herein operates on in-use information. The system includes a function as a service of a service provider that operates on encrypted data. The encrypted data includes encrypted in-use data. The system also includes a trusted execution environment (TEE) to operate within a cloud-based environment of a cloud provider. The function as a service operates on the encrypted data within the TEE, and the TEE protects service provider information from access by the cloud provider. The encrypted in-use data and the service provider information form at least a portion of the in-use information.