Abstract: Systems and techniques that facilitate universal and efficient privacy-preserving vertical federated learning are provided. In various embodiments, a key distribution component can distribute respective feature-dimension public keys and respective sample-dimension public keys to respective participants in a vertical federated learning framework governed by a coordinator, wherein the respective participants can send to the coordinator respective local model updates encrypted by the respective feature-dimension public keys and respective local datasets encrypted by the respective sample-dimension public keys. In various embodiments, an inference prevention component can verify a participant-related weight vector generated by the coordinator, based on which the key distribution component can distribute to the coordinator a functional feature-dimension secret key that can aggregate the encrypted respective local model updates into a sample-related weight vector.

Abstract: Techniques for federated learning are provided. A plurality of public encryption keys are distributed to a plurality of participants in a federated learning system, and a first plurality of responses is received from the plurality of participants, where each respective response of the first plurality of responses was generated based on training data local to a respective participant of the plurality of participants and is encrypted using a respective public encryption key of the plurality of public encryption keys. A first aggregation vector is generated based on the first plurality of responses, and a first private encryption key is retrieved using the first aggregation vector. An aggregated model is then generated based on the first private encryption key and the first plurality of responses.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate detection of an adversarial backdoor attack on a trained model at inference time are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a log component that records predictions and corresponding activation values generated by a trained model based on inference requests. The computer executable components can further comprise an analysis component that employs a model at an inference time to detect a backdoor trigger request based on the predictions and the corresponding activation values. In some embodiments, the log component records the predictions and the corresponding activation values from one or more layers of the trained model.

Abstract: A computer-implemented method of communicating in a federated learning environment includes an aggregator and a plurality of federated learning participants that respectively maintain their own data and communicate with the aggregator. The aggregator monitors the plurality of federated learning participants for factors associated with stragglers. The federated learning participants are assigned into tiers based on the monitoring of the factors. The aggregator queries the federated learning participants in a selected tier and designates late responders as stragglers. Maximum waiting time may be defined for each tier. The aggregator applies a predicted response for drop outs including collected participants' replies and computed predictions associated with the stragglers to update a training of a federated learning model. The federated learning participants that do not respond within the designated wait time are designated as drop outs.

Abstract: Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.

Abstract: A computer-implemented method includes receiving characteristics of available resources usable for downloading layers of a container image and fetching a manifest of the container image from a container registry. The method includes determining layers of the container image to be downloaded based on the manifest and, based on the characteristics of the available resources and sizes of the layers to be downloaded, adjusting an optimal parallelism to download the layers. The method includes downloading the layers.

Abstract: Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.

Abstract: Systems and methods are provided to support service management in cloud computing networks. For example, a method for managing a computing platform includes provisioning a plurality of virtual machines across a plurality of computing nodes of a computing platform, wherein the provisioned virtual machines are configured to execute service workloads to provide one or more services to customers, and utilizing one or more of the provisioned virtual machines which execute the service workloads to further perform service management operations for managing the computing platform. The service management operations for managing the computing platform include, e.g., mediation and rating operations for revenue management of the computing platform.

Abstract: Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.

Abstract: Systems and methods are provided to support service management in cloud computing networks. For example, a method for managing a computing platform includes provisioning a plurality of virtual machines across a plurality of computing nodes of a computing platform, wherein the provisioned virtual machines are configured to execute service workloads to provide one or more services to customers, and utilizing one or more of the provisioned virtual machines which execute the service workloads to further perform service management operations for managing the computing platform. The service management operations for managing the computing platform include, e.g., mediation and rating operations for revenue management of the computing platform.

Abstract: An on-demand hyperlink computer search tool implements a method of providing hyperlinked search results that overlay a computer document. The method includes selecting an object in a computer document, submitting the object to a search engine, receiving results, categorizing the results as clusters and displaying the clusters. The clusters are displayed in layers on the same page as the computer document and over the object. Furthermore, an “additional object” may be selected from the clusters. If the selected “additional object” hyperlinks to a specific website, the website is retrieved. If not, the method repeats the process iteratively until “another additional object” that is selected hyperlinks to a specific website.

Abstract: An on-demand hyperlink computer search tool implements a method of providing hyperlinked search results that overlay a computer document. The method includes selecting an object in a computer document, submitting the object to a search engine, receiving results, categorizing the results as clusters and displaying the clusters. The clusters are displayed in layers on the same page as the computer document and over the object. Furthermore, an “additional object” may be selected from the clusters. If the selected “additional object” hyperlinks to a specific website, the website is retrieved. If not, the method repeats the process iteratively until “another additional object” that is selected hyperlinks to a specific website.