Federated Learning over Next-Generation Ethernet Passive Optical Networks

2021/09/29 by Oscar J. Ciceri, Ciceri, Oscar J., Carlos A. Astudillo +5 · 1 citation
Engineering · #Advanced Optical Network Technologies #Advanced Photonic Communication Systems #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Optical Network Technologies #Performance (cs.PF)

paper · pdf · doi:10.48550/arxiv.2109.14593

openalex publication_date 2021/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated Learning (FL) is a distributed machine learning (ML) type of processing that preserves the privacy of user data, sharing only the parameters of ML models with a common server. The processing of FL requires specific latency and bandwidth demands that need to be fulfilled by the operation of the communication network. This paper introduces a Dynamic Wavelength and Bandwidth Allocation algorithm for Quality of Service (QoS) provisioning for FL traffic over 50 Gb/s Ethernet Passive Optical Networks. The proposed algorithm prioritizes FL traffic and reduces the delay of FL and delay-critical applications supported on the same infrastructure.

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• FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning

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