# US 2025/0156757 A1 · Client-based re-training and evaluation of artificial-intelligence model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0156757 A1 - Document ID: US20250156757A1 - Status: Published application - Kind: A1 - Application number: 18/910,364 - Filed: 2024-10-09 - Published: 2025-05-15 - Earliest claimed: 2023-11-14 - Assignees: Siemens Healthineers AG - Applicants: Siemens Healthineers AG - Inventors: Anna Jerebko; Varghese Kollerathu - Web page: https://patentgrep.com/patent/US20250156757A1 ## CPC - G06N 20/00: Machine learning - G16H 30/40: for processing medical images, e.g. editing - G06N 3/045: Combinations of networks - G06N 3/098: Distributed learning, e.g. federated learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present disclosure relates deployment of an artificial intelligence model from a central server. The disclosure further relates to client-based re-training and validation of an artificial intelligence model at multiple clients. ## Claims ### Claim 1 A computer-implemented method, comprising: deploying an artificial intelligence model in a first training state to multiple clients; at each of the multiple clients, acquiring a respective training dataset; at each of the multiple clients, re-training the artificial intelligence model to obtain the artificial intelligence model in a second training state; associating the multiple clients with multiple groups; for each of the multiple groups, aggregating weights of the artificial intelligence model in the second training state associated with clients within the respective group, to obtain the artificial intelligence model in a third training state; and evaluating the artificial intelligence model in each of the third training states. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the artificial intelligence model in each of the third training states is evaluated using a cross-evaluation process at the multiple clients. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the cross-evaluation process comprises a benchmark against performance of the artificial intelligence model in the first training state, the benchmark being based on the respective training dataset at the respective client. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein the cross-evaluation process includes providing the artificial intelligence model in the respective third training state associated with a given one of the multiple groups to at least one further group of the multiple groups and benchmarking the artificial intelligence model in the respective third training state associated with the given one of the multiple groups at each of the at least one further group. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: for each of the multiple groups, responsive to said evaluating of the artificial intelligence model in the respective third training state not meeting a predefined benchmark, re-associating the clients previously associated with the respective group with multiple newly-formed groups replacing the respective group and repeating said aggregating of the weights and evaluating for the newly formed groups. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising: at each one of the multiple clients, responsive to identifying at least one of the respective training data sets or the artificial intelligence model in the respective second training state as an outlier, suppressing an impact of the artificial intelligence model in the respective second training state onto said aggregating of the weights. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: bypassing said evaluating for at least one group comprising clients labeled as trustworthy. ### Claim 8 (depends on 1) The computer-implemented method of claim 1 wherein the groups are non-overlapping. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the clients are associated with the groups depending on one or more predefined criteria, the one or more predefined criteria comprises: an overall count of the groups; a number of clients per group; or a size of the training datasets at each of the multiple clients. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising: upon said evaluating of the artificial intelligence model in each of the third training states, aggregating weights of the artificial intelligence model in those third training states associated with a positive result of said evaluating, to obtain the artificial intelligence model in a fourth training state. ### Claim 11 (depends on 10) The computer-implemented method of claim 10 further comprising deploying the artificial intelligence model in the fourth training state. ### Claim 12 (depends on 1) The computer-implemented method of claim 1, wherein the artificial intelligence model performs one or more medical imaging processing tasks. ### Claim 13 A computer-implemented method for use in a client, comprising: obtaining, from a central authority, an artificial intelligence model in a first training state; performing inference based on the artificial intelligence model in the first training state; acquiring a training dataset based on said performing of the inference; re-training the artificial intelligence model based on the training dataset, to obtain the artificial intelligence model in a second training state; providing the artificial intelligence model in the second training state to at least one of the central authority and one or more further clients; establishing the artificial intelligence model in multiple third training states based on information obtained from at least one of the central authority or the one or more further clients; and evaluating the artificial intelligence model in each of the multiple third training states based on benchmarking against performance of the artificial intelligence model in the third training state and based on the training dataset. ### Claim 14 (depends on 13) The computer-implemented method of claim 13, wherein said establishing of the artificial intelligence model in the multiple third training states comprises obtaining the artificial intelligence model in the multiple third training states from the central authority. ### Claim 15 (depends on 13) The computer-implemented method of claim 13, wherein said establishing of the artificial intelligence model in the multiple third training states comprises: obtaining the artificial intelligence model in multiple second training states from the multiple further clients; associating the client and the multiple further clients with multiple groups; and for each of the multiple groups, aggregating weights of the artificial intelligence model in the second training state associated with clients within the respective group, to obtain the artificial intelligence model in the respective third training state. ### Claim 16 (depends on 15) The computer-implemented method of claim 15, wherein the client and the multiple further clients are associated with the multiple groups based on grouping information obtained from the central authority. ### Claim 17 A device, comprising: non-transitory computer-readable medium for storing program code; and one or more processor units in communication with the non-transitory computer-readable medium, the one or more processor units being operative with the program code to perform operations including obtaining, from a central authority, an artificial intelligence model in a first training state, performing inference based on the artificial intelligence model in the first training state, acquiring a training dataset based on said performing of the inference, re-training the artificial intelligence model based on the training dataset, to obtain the artificial intelligence model in a second training state, providing the artificial intelligence model in the second training state to at least one of the central authority and one or more further clients, establishing the artificial intelligence model in multiple third training states based on information obtained from at least one of the central authority or the one or more further clients, and evaluating the artificial intelligence model in each of the multiple third training states based on benchmarking against performance of the artificial intelligence model in the third training state and based on the training dataset. ### Claim 18 (depends on 17) The device of claim 17 wherein said establishing of the artificial intelligence model in the multiple third training states comprises obtaining the artificial intelligence model in the multiple third training states from the central authority. ### Claim 19 (depends on 17) The device of claim 17, wherein said establishing of the artificial intelligence model in the multiple third training states comprises: obtaining the artificial intelligence model in multiple second training states from the multiple further clients; associating the client and the multiple further clients with multiple groups; and for each of the multiple groups: aggregating weights of the artificial intelligence model in the second training state associated with clients within the respective group, to obtain the artificial intelligence model in the respective third training state. ### Claim 20 (depends on 19) The device of claim 19, wherein the client and the multiple further clients are associated with the multiple groups based on grouping information obtained from the central authority.