TY - GEN
T1 - FedBed
T2 - 16th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2023
AU - Symeonides, Moysis
AU - Nikolaidis, Fotis
AU - Trihinas, Demetris
AU - Pallis, George
AU - Dikaiakos, Marios D.
AU - Bilas, Angelos
N1 - Publisher Copyright:
© 2023 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/12/4
Y1 - 2023/12/4
N2 - Federated Learning has become the de facto paradigm for training AI models under a distributed modality where the computational effort is spread across several clients without sharing local data. Despite its distributed nature, enabling FL in an Edge-Cloud continuum is challenging with resource and network heterogeneity, different AI models and libraries, and non-uniform data distributions, all hampering QoS and limiting innovation potential. This work introduces FedBed, a testing framework that enables the rapid and reproducible benchmarking of FL deployments on virtualized testbeds. FedBed aids users in assessing the numerous trade-offs that result from combining a variety of FL software and infrastructure configurations in Edge-Cloud settings. This reduces the time-consuming process that includes the setup of either a virtual physical or emulation testbed, experiment configurations, and the monitoring of the resulting FL testbed.
AB - Federated Learning has become the de facto paradigm for training AI models under a distributed modality where the computational effort is spread across several clients without sharing local data. Despite its distributed nature, enabling FL in an Edge-Cloud continuum is challenging with resource and network heterogeneity, different AI models and libraries, and non-uniform data distributions, all hampering QoS and limiting innovation potential. This work introduces FedBed, a testing framework that enables the rapid and reproducible benchmarking of FL deployments on virtualized testbeds. FedBed aids users in assessing the numerous trade-offs that result from combining a variety of FL software and infrastructure configurations in Edge-Cloud settings. This reduces the time-consuming process that includes the setup of either a virtual physical or emulation testbed, experiment configurations, and the monitoring of the resulting FL testbed.
KW - edge computing
KW - federated learning
UR - http://www.scopus.com/inward/record.url?scp=85191658062&partnerID=8YFLogxK
U2 - 10.1145/3603166.3632138
DO - 10.1145/3603166.3632138
M3 - Conference contribution
AN - SCOPUS:85191658062
T3 - 16th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2023
BT - 16th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2023
PB - Association for Computing Machinery, Inc
Y2 - 4 December 2023 through 7 December 2023
ER -