Abstract: An example apparatus disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of execute or instantiate the machine readable instructions to send a global model to one or more collaborator models, the one or more collaborator models training on a local dataset associated with a collaborator model, receive one or more collaborator models trained on the local dataset, compute a similarity measurement between the global model and at least one collaborator model, determine aggregation for the global model based on the computed similarity measurement, aggregate one or more one or more collaborator models based on the determined aggregation, and update the global model based on the aggregation.

Abstract: Methods, systems, apparatus, and articles of manufacture to augment training data based on synthetic images are disclosed. An example apparatus disclosed herein includes programmable circuitry to generate, with one or more first layers of a generative adversarial network (GAN), a latent representation corresponding to a first image representative of a first racial domain, generate, with one or more second layers of the GAN, a second image based on the latent representation, the second image corresponding to a second racial domain different from the first racial domain, and augment a training dataset based on the second image.

Abstract: Methods, apparatus, and systems are disclosed for mapping active assurance intents to resource orchestration and life cycle management. An example apparatus disclosed herein is to reserve a probe on a compute device in a cluster of compute devices based on a request to satisfy a resource availability criterion associated with a resource of the cluster, apply a risk mitigation operation based on the resource availability criterion before deployment of a workload to the cluster, and monitor whether the criterion is satisfied based on data from the probe after deployment of the workload to the cluster.

Abstract: Embodiments for automatically tuning heterogenous wireless networks are disclosed herein. In one example, performance data is received for multiple wireless networks. The wireless networks are based on multiple wireless technologies, and the performance data is based on multiple layers of the protocol stacks of the wireless technologies. The performance data is used to determine one or more configuration settings to adjust for one or more of the wireless networks. The determined configuration setting(s) are then adjusted.