Back to Search

Time-correlated sparsification for communication-efficient federated learning

Title	Time-correlated sparsification for communication-efficient federated learning
Author	Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.
Publication Date:	2021
Publication Place	- IEEE
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-153868209-8
Record ID	61750359-461f-4a47-a58f-c465853dc910
Date	2021
Notes	Engineering and Physical Sciences Research Council ; European Research Council
Sample Text	Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained models, the communication load due to the iterative exchanges between the clients and the PS often becomes a bottleneck in the performance. Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS. In this paper, we introduce a novel time-correlated sparsification (TCS) scheme, which builds upon the notion that sparse communication framework can be considered as identifying the most significant elements of the underlying model. Hence, TCS exploits the correlation between the sparse representations at consecutive iterations in FL, so that the overhead due to encoding of the sparse representation can be significantly reduced without compromising the test accuracy. Through extensive simulations on the CIFAR-10 dataset, we show that TCS can achieve centralized training accuracy with 100 times sparsification, and up to 2000 times reduction in the communication load when employed with quantization.
DOI	10.1109/ISIT45174.2021.9518221

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Time-correlated sparsification for communication-efficient federated learning

Author Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.

Publication Date 2021

Publication Place - IEEE

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-153868209-8

Record ID 61750359-461f-4a47-a58f-c465853dc910

Date 2021

Notes Engineering and Physical Sciences Research Council ; European Research Council

Sample Text Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained models, the communication load due to the iterative exchanges between the clients and the PS often becomes a bottleneck in the performance. Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS. In this paper, we introduce a novel time-correlated sparsification (TCS) scheme, which builds upon the notion that sparse communication framework can be considered as identifying the most significant elements of the underlying model. Hence, TCS exploits the correlation between the sparse representations at consecutive iterations in FL, so that the overhead due to encoding of the sparse representation can be significantly reduced without compromising the test accuracy. Through extensive simulations on the CIFAR-10 dataset, we show that TCS can achieve centralized training accuracy with 100 times sparsification, and up to 2000 times reduction in the communication load when employed with quantization.

DOI 10.1109/ISIT45174.2021.9518221