PuSH - Publication Server of Helmholtz Zentrum München: Utility-preserving federated learning.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Nasirigerdeh, R.* ; Rueckert, D.* ; Kaissis, G.

Utility-preserving federated learning.

In: (AISec 2023 - Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 30 November 2023, Copenhagen, Denmark). 2023. 55-65 (AISec 2023 - Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security)

DOI

Abstract
Metrics
Extra information

We investigate the concept of utility-preserving federated learning (UPFL) in the context of deep neural networks. We theoretically prove and experimentally validate that UPFL achieves the same accuracy as centralized training independent of the data distribution across the clients. We demonstrate that UPFL can fully take advantage of the momentum and weight decay techniques compared to centralized training, but it incurs substantial communication overhead. Ordinary federated learning, on the other hand, provides much higher communication efficiency, but it can partially benefit from the aforementioned techniques to improve utility. Given that, we propose a method called weighted gradient accumulation to gain more benefit from the momentum and weight decay akin to UPFL, while providing practical communication efficiency similar to ordinary federated learning.

Altmetric