FedADC: Accelerated federated learning with drift control

Name: FedADC: Accelerated federated learning with drift control
Author: Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.

İsim	FedADC: Accelerated federated learning with drift control
Yazar	Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.
Basım Tarihi:	2021
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5386-8209-8
Kayıt Numarası	4efdcd74-194b-4ad5-9c49-916da91eb333
Tarih	2021
Notlar	Engineering and Physical Sciences Research Council ; European Research Council
Örnek Metin	Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.
DOI	10.1109/ISIT45174.2021.9517850

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FedADC: Accelerated federated learning with drift control

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

Basım Tarihi 2021

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5386-8209-8

Kayıt Numarası 4efdcd74-194b-4ad5-9c49-916da91eb333

Tarih 2021

Notlar Engineering and Physical Sciences Research Council ; European Research Council

Örnek Metin Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.

DOI 10.1109/ISIT45174.2021.9517850