Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity. This architectural prevents the aggregator from being a single point-of-failure, and serves to protect the model even if all aggregators are compromised.

Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

In one aspect there is provided a method. The method may include collecting one or more functions that implement the decision logic of a solution. A snapshot of the one or more functions can be generated. The snapshot can executable code associated with the one or more functions. The solution can be deployed by at least storing the snapshot of the one or more functions to a repository Systems and articles of manufacture, including computer program products, are also provided.

Provided is a method, performed by a server, of performing distributed learning. The server builds a computer cluster by selecting worker nodes that are to perform distributed learning, from among a plurality of nodes, wherein nodes in the computer cluster include the server that is a master node and the worker nodes. The server identifies, with respect to each of the nodes in the computer cluster, an operation time taken for each of the nodes in the computer cluster to perform training, and adjusts a number of data included in each of data subsets, based on the operation time of each of the nodes in the computer cluster, the data subsets being used in training of the nodes in the computer cluster.

A device, system, and method use predictive analytics based on an application-centric approach. The method includes receiving a request from an interactive application interacting with a user utilizing a user device for action data indicating an action to be taken by the interactive application during a session with the user device, the request generated by the interactive application based on a decision point associated with the interactive application. The method includes receiving from the interactive application session data associated with the session and the user device. The method includes determining scoring package data associated with the request based on the session data, the scoring package data comprising a predictive model indicative of a plurality of actions to be performed by the interactive application. The method includes generating the action data based on the scoring package data and transmitting the action data to the interactive application.

A device, system, and method use predictive analytics based on an application-centric approach. The method includes receiving a request from an interactive application interacting with a user utilizing a user device for action data indicating an action to be taken by the interactive application during a session with the user device, the request generated by the interactive application based on a decision point associated with the interactive application. The method includes receiving from the interactive application session data associated with the session and the user device. The method includes determining scoring package data associated with the request based on the session data, the scoring package data comprising a predictive model indicative of a plurality of actions to be performed by the interactive application. The method includes generating the action data based on the scoring package data and transmitting the action data to the interactive application.

Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.