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3. Supporting Efficient Workflow Deployment of Federated Learning Systems across the Computing Continuum

Author

Prigent, Cédric, Antoniu, Gabriel, Costan, Alexandru, Cudennec, Loïc, Scalable Storage for Clouds and Beyond (KerData), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), DGA Maîtrise de l'information (DGA.MI), and Direction générale de l'Armement (DGA)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Federated Learning, Hyperparameter optimization, Computing Continuum, Workflow
Abstract

International audience; IoT devices produce ever growing amounts of data. Traditional cloud-based approaches for processing data are facing some limitations: bandwidth might become a bottleneck and sensitive data should not leave user devices as stated by data protection regulators such as GDPR. Federated Learning (FL) is a distributed Machine Learning paradigm aiming to collaboratively learn a shared model while considering privacy preservation. Clients do the training process locally with their private data while a central server updates the global model by aggregating local models. In the Computing Continuum context (edge-fog-cloud ecosystem), FL raises several challenges such as supporting very heterogeneous devices and optimizing massively distributed applications. We propose a workflow to better support and optimize FL systems across the Computing Continuum by relying on formal descriptions of the underlying infrastructure, hyperparameter optimization and model retraining in case of performance degradation. We motivate our approach by providing preliminary results using a human activity recognition dataset showing the importance of hyperparameter optimization and model retraining in the FL scenario.

Published
2022

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