公开(公告)号：US20240119307A1

公开(公告)日：2024-04-11

申请号：US18474934

申请日：2023-09-26

Applicant: Google LLC

Inventor： Hong-You Chen ,  Boqing Gong ,  Mingda Zhang ,  Hang Qi ,  Xuhui Jia ,  Li Zhang

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: The embodiments are directed towards providing personalized federated learning (PFL) models via sharable federated basis models. A model architecture and learning algorithm for PFL models is disclosed. The embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. The set of basis models are shared with each client of a set of the clients. Thus, the set of basis models is common to each client of the set of clients. However, each client may generate a unique PFL based on their specifically learned combination coefficients. The unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.

公开(公告)号：US20240330705A1

公开(公告)日：2024-10-03

申请号：US18579089

申请日：2021-07-12

Applicant: Google LLC

Inventor： Hang Qi ,  Sagar Manohar Waghmare ,  Tomer Meron

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: Example aspects of the present disclosure provide a novel, resource-efficient approach for learning image representation with federated learning, which can be referred to as federated sampled SoftMax. According to example aspects of the present disclosure, the federated learning clients sample a set of negative classes and optimize only the corresponding model parameters with respect to a sampled SoftMax objective that approximates the global full SoftMax objective. This approach significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full SoftMax method. This creates a possibility for efficiently learning image representations on decentralized data with a large number of classes in a privacy preserving way.