International Business Machines Corporation

Wei-Han Lee

Bong Jun Ko

Shiqiang Wang

Changchang Liu

Seraphin Bernard Calo

Dinesh C Verma

Jenny S Li

Nancy Anne Greco

Nirmit V Desai

Tova Roth

Luis Angel Bathen

Dhiraj Joshi

Gwilym Benjamin Lee Newton

Tiffany Tuor

Sujoy Kumar Roy Chowdhury

Prakriti Pritmani

Aakash Praliya

Yuang Jiang

Satish Rajani

Shiva G

Kay Murphy

Shilpa Shenai

Arun Yashwant Ingale

Raghu Ramaswamy

Ajjay Ratnakar

Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Dynamic use of artificial intelligence (AI) models on an autonomous AI enabled robotic device

A computer-implemented method, a computer program product, and a computer system for incremental and decentralized pruning of a machine learning model in federated learning. A federated learning system determines a serial sequence of participating in model pruning by agents in the federated learning system. A server in the federated learning system sends, to a first agent in the serial sequence, an initial model to trigger a federated pruning process for the machine learning model. The each of agents in the serial sequence prunes the machine learning model. The each of agents in the serial sequence generates an intermediately pruned model for an immediately next agent to prune. A final agent in the serial sequence prunes the machine learning model and generates a finally pruned model. The final agent sends the finally pruned model to the server.

Incremental and decentralized model pruning in federated machine learning

A computer-implemented method, a computer program product, and a computer system for enhanced distributed machine learning. A fusion server in a distributed machine learning system determines correlation relationships across agents in the distributed machine learning system, based on auxiliary information. The fusion server clusters the agents to form one or more communities, based on the correlation relationships. The fusion server selects, from the one or more communities, participating agents that participate in the enhanced distributed machine learning.

Leveraging correlation across agents for enhanced distributed machine learning

A method, a computer program product, and a computer system determine when to perform a federated learning process. The method includes identifying currently available contributors among contributors of a federated learning task for which the federated learning process is to be performed. The method includes determining a usefulness metric of the currently available contributors for respective datasets from each of the currently available contributors used in performing the federated learning process. The method includes, as a result of the usefulness metric of the currently available contributors being at least a usefulness threshold, generating a recommendation to perform the federated learning process with the datasets of the currently available contributors. The method includes transmitting the recommendation to a processing component configured to perform the federated learning process.

Federated learning of clients

Regulating a flow of data from an electronic device comprises generating a user profile associated with a user. Further, a resource profile is generated and is associated with one or more resources external to the electronic device. Additionally, a context profile is generated and describes an outcome of one or more previous interactions between the user of the electronic device and one or more resources. Rules are generated based on a comparison the user profile, the context profile and the site profile. Further, data sent from an electronic device is modified based on the set of rules.

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Wei-Han Lee