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.