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UID:submissions.supercomputing.org_SC22_sess275_rpost111@linklings.com
SUMMARY:Supporting Efficient Workflow Deployment of Federated Learning Sys
 tems on the Computing Continuum
DESCRIPTION:Posters, Research Posters\n\nSupporting Efficient Workflow Dep
 loyment of Federated Learning Systems on the Computing Continuum\n\nPrigen
 t, Antoniu, Costan, Cudennec\n\nFederated Learning (FL) is a distributed M
 achine Learning paradigm aiming to collaboratively learn a shared model wh
 ile considering privacy preservation by letting the clients process their 
 private data locally.  In the Computing Continuum context (edge-fog-cloud 
 ecosystem), FL raises several challenges such as supporting very heterogen
 eous devices and optimizing massively distributed applications.\n\nWe prop
 ose a workflow to better support and optimize FL systems across the Comput
 ing Continuum by relying on formal descriptions of the infrastructure, hyp
 erparameter optimization and model retraining in case of performance degra
 dation. We motivate our approach by providing preliminary results using a 
 human activity recognition dataset. The next objective will be to implemen
 t and deploy our solution on the Grid’5000 testbed.\n\nDuring the poster s
 ession, I will start by presenting the main problems for applying FL in th
 e Computing Continuum and how our approach is tackling it. Next I will pre
 sent preliminary results and discuss the remaining challenges.\n\nRegistra
 tion Category: Tech Program Reg Pass, Exhibits Reg Pass
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