公开(公告)号：US20250036961A1

公开(公告)日：2025-01-30

申请号：US18227535

申请日：2023-07-28

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Ganghua WANG ,  Ali PAYANI ,  Ramana Rao V. R. KOMPELLA

IPC: G06N3/098 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776

Abstract: In one embodiment, a supervisory device in a federated learning system generates an aggregated model that aggregates a plurality of machine learning models trained by trainer nodes in a federated learning system during a training round. The supervisory device computes an accuracy loss metric for the aggregated model. The supervisory device also computes a fairness loss metric for the aggregated model based on fairness-related metrics associated with the plurality of machine learning models trained by the trainer nodes. The supervisory device initiates an additional training round during which the trainer nodes retrain their machine learning models for aggregation by the apparatus, in accordance with a constrained optimization problem that seeks to optimize a tradeoff between accuracy and fairness associated with the aggregated model.