Fast Server Learning Rate Tuning for Coded Federated Dropout

Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic, Gerald Q. Maguire

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In Federated Learning (FL), clients with low computational power train a common machine model by exchanging parameters via updates instead of transmitting potentially private data. Federated Dropout (FD) is a technique that improves the communication efficiency of a FL session by selecting a subset of model parameters to be updated in each training round. However, compared to standard FL, FD produces considerably lower accuracy and faces a longer convergence time. In this chapter, we leverage coding theory to enhance FD by allowing different sub-models to be used at each client. We also show that by carefully tuning the server learning rate hyper-parameter, we can achieve higher training speed while also reaching up to the same final accuracy as the no dropout case. Evaluations on the EMNIST dataset show that our mechanism achieves 99.6% of the final accuracy of the no dropout case while requiring 2.43 × less bandwidth to achieve this level of accuracy.
Original languageEnglish (US)
Title of host publicationTrustworthy Federated Learning
PublisherSpringer International Publishing
Number of pages16
ISBN (Print)9783031289958
StatePublished - Mar 29 2023
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2023-05-24
Acknowledged KAUST grant number(s): ORA-CRG2021-4699
Acknowledgements: This research work was conducted with funding awarded by the Swedish Research Council for the project “Scalable Federated Learning” with registration number 2021-04610. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No. ORA-CRG2021-4699.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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