Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Du...

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Vydáno v:	IEEE journal on selected areas in communications Ročník 37; číslo 6; s. 1205 - 1221
Hlavní autoři:	Wang, Shiqiang, Tuor, Tiffany, Salonidis, Theodoros, Leung, Kin K., Makaya, Christian, He, Ting, Chan, Kevin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive systems Algorithms Artificial intelligence Computer simulation Control algorithms Control theory Convergence Data models Distributed databases Distributed machine learning Edge computing Experimentation Federated learning Machine learning Machine learning algorithms Mathematical models mobile edge computing New technology Parameters Peer-to-peer computing Training wireless networking
ISSN:	0733-8716, 1558-0008
On-line přístup:	Získat plný text
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Abstract	Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
AbstractList	Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
Author	Salonidis, Theodoros Makaya, Christian Tuor, Tiffany Leung, Kin K. He, Ting Chan, Kevin Wang, Shiqiang
Author_xml	– sequence: 1 givenname: Shiqiang orcidid: 0000-0003-2090-5512 surname: Wang fullname: Wang, Shiqiang email: wangshiq@us.ibm.com organization: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA – sequence: 2 givenname: Tiffany surname: Tuor fullname: Tuor, Tiffany email: tiffany.tuor14@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, London, U.K – sequence: 3 givenname: Theodoros surname: Salonidis fullname: Salonidis, Theodoros email: tsaloni@us.ibm.com organization: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA – sequence: 4 givenname: Kin K. surname: Leung fullname: Leung, Kin K. email: kin.leung@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, London, U.K – sequence: 5 givenname: Christian surname: Makaya fullname: Makaya, Christian email: chrismak@ieee.org organization: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA – sequence: 6 givenname: Ting orcidid: 0000-0003-1070-7483 surname: He fullname: He, Ting email: t.he@cse.psu.edu organization: Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA – sequence: 7 givenname: Kevin orcidid: 0000-0002-6425-5403 surname: Chan fullname: Chan, Kevin email: kevin.s.chan.civ@mail.mil organization: Army Research Laboratory, Adelphi, MD, USA
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Snippet	Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge....
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SubjectTerms	Adaptive systems Algorithms Artificial intelligence Computer simulation Control algorithms Control theory Convergence Data models Distributed databases Distributed machine learning Edge computing Experimentation Federated learning Machine learning Machine learning algorithms Mathematical models mobile edge computing New technology Parameters Peer-to-peer computing Training wireless networking
Title	Adaptive Federated Learning in Resource Constrained Edge Computing Systems
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