Method and apparatus to enable federated learning job management services
Patent Information
- Application Number
- HK62026126841
- Authority / Receiving Office
- HK · HK
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-06-09
- Filing Date
- 2026-07-31
- Publication Date
- 2026-10-09
- Estimated Expiration
- 2044-06-06
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Federated Learning (FL) can be deployed as a service by defining FL job management capabilities. An FL service can have the following capabilities: receiving FL job specifications from FL users, processing and managing these FL jobs on behalf of FL users through interaction with FL clients, and updating the status of FL jobs to FL users during the job processing. Summary
Claims
CLAIMSWhat is claimed is:
1. An apparatus associated with a federated learning (FL) service, the apparatus comprising one or more processors and memory storing instructions which, when executed by the one or more processors, cause the apparatus to: receive, from an FL user, a request to create or update an FL job, wherein the request comprises FL job information indicating one or more machine learning (ML) requirements and one or more FL training requirements; based on the FL job information, perform at least one of: determining whether the FL user is authorized to perform the FL job, selecting one or more FL clients that meet one or more FL training requirements, or determining one or more FL tasks, associated with the FL job, to be performed by the one or more FL clients; send, to the FL user, a response comprising a FL job ID and FL request status information; send, to the one or more FL clients, one or more FL task requests comprising FL task information; receive, from the one or more FL clients, FL task status information; based on the received FL task status information, determine whether to perform one or more FL training rounds; and send, to the FL user, FL job status information.
2. The apparatus of claim 1, wherein the determination whether to perform one or more FL training rounds is based on aggregating the received FL task status information and comparing it to ML requirements specified in the FL job by the FL user.
3. The apparatus of claim 1, wherein the one or more FL training requirements comprise at least one of a specified operating environment, an FL framework, an algorithmname and version, one or more runtime requirements, one or more processor and memory requirements, a minimum number of sample data, or a maximum age of data.
4. The apparatus of claim 1, wherein the FL job information further indicates an FL client configuration indicating at least one of: an FL client ID list, one or more FL client training requirements, or an FL client data filter.
5. The apparatus of claim 1, wherein the FL job status information indicates at least one of: a status indicating whether the FL job has not started, has started, has paused, has resumed, has stopped, has been cancelled, has completed, or a current FL round number.
6. The apparatus of claim 1, wherein the one or more ML requirements indicate at least one of: one or more ML algorithms, an ML problem type, a multi-model training mode, one or more hyperparameters, a model evaluation function, model initialization, an aggregation algorithm, a minimum number of FL training rounds, a minimum number of FL clients, or a minimum number of total data instances.
7. The apparatus of claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: determine if or when to start the FL job.
8. The apparatus of claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: send, to the one or more FL clients, FL task subscription or retrieve requests to obtain FL task results and the FL task status information.
9. The apparatus of claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: receive, from one or more FL users, a FL job subscription or retrieval request to obtain FL job results and status information of interest to the one or more FL users.
10. The apparatus of claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: send, to the FL user, a notification indicating an FL related event.
11. An apparatus associated with a federated learning (FL) client, the apparatus comprising one or more processors and memory storing instructions which, when executed by the one or more processors, cause the apparatus to: receive, from an FL service, one or more FL task requests comprising FL task information, wherein the FL task information indicates one or more machine learning (ML) requirements and one or more FL training requirements; perform, based on the one or more FL task requests, one or more FL tasks; send, to the FL service, one or more notifications comprising FL task status information.
12. The apparatus of claim 11, wherein the one or more FL training requirements comprise at least one of a specified operating environment, an FL framework, an algorithm name and version, one or more runtime requirements, one or more processor and memory requirements, a minimum number of sample data, or a maximum age of data.
13. The apparatus of claim 11, wherein the FL job information further indicates an FL client configuration indicating at least one of: an FL client ID list, one or more FL client training requirements, or an FL client data filter.
14. The apparatus of claim 11, wherein the FL job status information indicates at least one of: a status indicating whether the FL job has not started, has started, has paused, has resumed, has stopped, has been cancelled, has completed, or a current FL round number.
15. A method comprising: receiving, from a federal learning (FL) user, a request to create or update an FL job, wherein the request comprises FL job information indicating one or more machine learning (ML) requirements and one or more FL training requirements; based on the FL job information, performing at least one of:determining whether the FL user is authorized to perform the FL job, selecting one or more FL clients that meet one or more FL training requirements, or determining one or more FL tasks, associated with the FL job, to be performed by the one or more FL clients; sending, to the FL user, a response comprising a FL job ID and FL request status information; sending, to the one or more FL clients, one or more FL task requests comprising FL task information; receiving, from the one or more FL clients, FL task status information; based on the received FL task status information, determining whether to perform one or more FL training rounds; and sending, to the FL user, FL job status information.
16. The method of claim 15, wherein the determination whether to perform one or more FL training rounds is based on aggregating the received FL task status information and comparing it to ML requirements specified in the FL job by the FL user.
17. The method of claim 15, wherein the one or more FL training requirements comprise at least one of a specified operating environment, an FL framework, an algorithm name and version, one or more runtime requirements, one or more processor and memory requirements, a minimum number of sample data, or a maximum age of data.
18. The method of claim 15, wherein the FL job information further indicates an FL client configuration indicating at least one of an FL client ID list, one or more FL client training requirements, or an FL client data filter.
19. The method of claim 15, wherein the FL job status information indicates at least one of a status indicating whether the FL job has not started, has started, has paused, has resumed, has stopped, has been cancelled, has completed, or a current FL round number.
20. The method of claim 15, wherein the one or more ML requirements indicate at least one of: one or more ML algorithms, an ML problem type, a multi-model training mode, one or more hyperparameters, a model evaluation function, model initialization, an aggregation algorithm, a minimum number of training rounds, a minimum number of FL clients, or a minimum number of total data instances.