Federated Learning Over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL imp...

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Vydáno v:	IEEE journal on selected areas in communications Ročník 39; číslo 12; s. 3723 - 3741
Hlavní autoři:	Xing, Hong, Simeone, Osvaldo, Bi, Suzhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Channel noise Clients Cloud computing Collaborative work Convergence Convexity D2D networks Data centers Data models decentralized stochastic gradient descent Device-to-device communication distributed learning Federated learning Image classification Internet of Things Machine learning over-the-air computation random linear coding Signal to noise ratio Stochastic processes Topology Wireless communications Wireless networks
ISSN:	0733-8716, 1558-0008
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Abstract	The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task.
AbstractList	The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task.
Author	Xing, Hong Simeone, Osvaldo Bi, Suzhi
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SubjectTerms	Algorithms Channel noise Clients Cloud computing Collaborative work Convergence Convexity D2D networks Data centers Data models decentralized stochastic gradient descent Device-to-device communication distributed learning Federated learning Image classification Internet of Things Machine learning over-the-air computation random linear coding Signal to noise ratio Stochastic processes Topology Wireless communications Wireless networks
Title	Federated Learning Over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
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