Federated Learning over Next-Generation Ethernet Passive Optical Networks

Federated Learning (FL) is a distributed machine learning 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 must be fulfilled by the operation of the com...

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Bibliographic Details
Published in:IEEE network 2023-01, Vol.37 (1), p.70-76
Main Authors: Ciceri, Oscar J., Astudillo, Carlos A., Zhu, Zuqing, da Fonseca, Nelson L. S.
Format: Article
Language:English
Subjects:
Algorithms
Bandwidth
Bandwidths
Computational modeling
Delays
Ethernet
Federated learning
Heuristic algorithms
Machine learning
Multiplexing
Network latency
Optical communication
Provisioning
Quality of service
Resource management
Servers
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Summary:Federated Learning (FL) is a distributed machine learning 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 must be fulfilled by the operation of the communication network. This article introduces two Dynamic Wavelength and Bandwidth Allocation algorithms for TWDM-PONs: one based on bandwidth reservation and the other on statistical multiplexing for the Quality of Service provisioning for FL traffic over 50 Gb/s Ethernet Passive Optical Networks.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.111.2100716