Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Upgrade of Deep Neural Network-based Optical Monitors by Communication-Efficient Federated Learning

Not Accessible

Your library or personal account may give you access

Abstract

We present an efficient scheme to upgrade DNN-based optical monitors collaboratively trained through multiple network operators without revealing each confidential data, applying federated learning with pre-model size reduction based on transferable lottery ticket hypothesis.

© 2023 The Author(s)

PDF Article
More Like This
Network for AI: Communication-Efficient Federated Learning with MST-based Scheduling and Multi-Aggregation over Optical Networks

Ruikun Wang, Jiawei Zhang, Memedhe Ibrahimi, Zhiqun Gu, Yuming Xiao, Francesco Musumeci, Massimo Tornatore, and Yuefeng Ji
Tu3B.3 Optical Fiber Communication Conference (OFC) 2024

High-Precision Edge-Cloud Collaboration with Federated Learning in Edge Optical Network

Chao Li, Hui Yang, Quiyan Yao, Zhengjie Sun, and Jie Zhang
W6A.11 Optical Fiber Communication Conference (OFC) 2021

Scalable Federated Learning over Passive Optical Networks

Jun Li, Lei Chen, and Jiajia Chen
W6A.36 Optical Fiber Communication Conference (OFC) 2021

Poster Presentation

Media 1: PDF (473 KB)     
Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.