Towards Federated Learning using FaaS Fabric

Mohak Chadha, Anshul Jindal, Michael Gerndt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a shared model for heterogeneous devices such as resource-constrained edge and cloud makes the efficient management of FL-clients challenging. Furthermore, with the rapid growth of FL-clients, the scaling of FL training process is also difficult. In this paper, we propose a possible solution to these challenges: federated learning over a combination of connected Function-as-a-Service platforms, i.e., FaaS fabric offering a seamless way of extending FL to heterogeneous devices. Towards this, we present FedKeeper, a tool for efficiently managing FL over FaaS fabric. We demonstrate the functionality of FedKeeper by using three FaaS platforms through an image classification task with a varying number of devices/clients, different stochastic optimizers, and local computations (local epochs).

Original languageEnglish
Title of host publicationWOSC 2020 - Proceedings of the 2020 6th International Workshop on Serverless Computing, Part of Middleware 2020
PublisherAssociation for Computing Machinery, Inc
Pages49-54
Number of pages6
ISBN (Electronic)9781450382045
DOIs
StatePublished - 7 Dec 2020
Event6th International Workshop on Serverless Computing, WOSC 2020 - Part of Middleware 2020 - Virtual, Online, Netherlands
Duration: 7 Dec 202011 Dec 2020

Publication series

NameWOSC 2020 - Proceedings of the 2020 6th International Workshop on Serverless Computing, Part of Middleware 2020

Conference

Conference6th International Workshop on Serverless Computing, WOSC 2020 - Part of Middleware 2020
Country/TerritoryNetherlands
CityVirtual, Online
Period7/12/2011/12/20

Keywords

  • FaaS
  • FaaS platforms
  • Federated learning
  • Function-as-a-service
  • Neural networks
  • Serverless

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