Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
2022
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
8
27-34
27-34
Series
IEEE INTELLIGENT SYSTEMS, Volume 37, issue 2
Abstract
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This article introduces two approaches for improving communication efficiency by dynamic sampling and top-k selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-k largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods' effectiveness.Description
Publisher Copyright: Author
Keywords
Collaborative work, Computational modeling, Costs, Heuristic algorithms, Servers, Training, Transportation
Other note
Citation
Ji, S, Jiang, W, Walid, A & Li, X 2022, ' Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning ', IEEE Intelligent Systems, vol. 37, no. 2, pp. 27-34 . https://doi.org/10.1109/MIS.2021.3114610