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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Veröffentlicht in:IEEE journal on selected areas in communications Jg. 37; H. 6; S. 1205 - 1221
Hauptverfasser: Wang, Shiqiang, Tuor, Tiffany, Salonidis, Theodoros, Leung, Kin K., Makaya, Christian, He, Ting, Chan, Kevin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2904348