公开(公告)号：US20240012680A1

公开(公告)日：2024-01-11

申请号：US17874182

申请日：2022-07-26

Applicant: VMware, Inc.

Inventor： Fangchi Wang ,  Hai Ning Zhang ,  Layne Lin Peng ,  Renming Zhao ,  Siyu Qiu

IPC: G06F9/48 ,  G06F9/50 ,  G06F9/445

CPC classification number: G06F9/4881 ,  G06F9/5072 ,  G06F9/44505

Abstract: Techniques for facilitating inter-cloud federated learning (FL) are provided. In one set of embodiments, these techniques comprise an FL lifecycle manager that enables users to centrally manage the lifecycles of FL components across different cloud platforms. The lifecycle management operations enabled by the FL lifecycle manager can include deploying/installing FL components on the cloud platforms, updating the components, and uninstalling the components. In a further set of embodiments, these techniques comprise an FL job manager that enables users to centrally manage the execution of FL training runs (i.e., FL jobs) on FL components that have been deployed via the FL lifecycle manager. For example, the FL job manager can enable users to define the parameters and configuration of an FL job, initiate the job, monitor the job's status, take actions on the running job, and collect the job's results.

公开(公告)号：US20230229640A1

公开(公告)日：2023-07-20

申请号：US17580574

申请日：2022-01-20

Applicant: VMware, Inc.

Inventor： Layne Lin Peng ,  Hai Ning Zhang ,  Jia Hao Chen ,  Fangchi Wang

IPC: G06F16/21 ,  G06N20/00 ,  G06F16/27

CPC classification number: G06F16/211 ,  G06N20/00 ,  G06F16/27

Abstract: A collaborative data schema management system for federated learning (i.e., federated data manager (FDM)) is provided. Among other things, FDM enables the members of a federated learning alliance to (1) propose data schemas for use by the alliance, (2) identify and bind local datasets to proposed schemas, (3) create, based on the proposed schemas, training datasets for addressing various ML tasks, and (4) control, for each training dataset, which of the local datasets bound to that training dataset (and thus, which alliance members) will actually participate in the training of a particular ML model. FDM enables these features while ensuring that the contents of the members' local datasets remain hidden from each other, thereby preserving the privacy of that data.