2023
DOI: 10.1109/tcomm.2023.3253718
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Decentralized Aggregation for Energy-Efficient Federated Learning via D2D Communications

Abstract: It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. However, off-the-shelf FL models aggregate global parameters at a central parameter server (CPS), incre… Show more

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Cited by 20 publications
(3 citation statements)
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“…In [26], the authors developed a multi-stage hybrid FL approach, which orchestrated the devices on different network layers in a collaborative D2D manner to form consensus on the learning model parameters. The authors in [27] introduced a decentralized FL scheme that took D2D communications and overlapped clustering to enable decentralized device aggregation and to save energy consumption. In [28], the authors focused on the wireless communication efficiency of D2D FL, and they presented theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent in FL systems.…”
Section: Related Workmentioning
confidence: 99%
“…In [26], the authors developed a multi-stage hybrid FL approach, which orchestrated the devices on different network layers in a collaborative D2D manner to form consensus on the learning model parameters. The authors in [27] introduced a decentralized FL scheme that took D2D communications and overlapped clustering to enable decentralized device aggregation and to save energy consumption. In [28], the authors focused on the wireless communication efficiency of D2D FL, and they presented theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent in FL systems.…”
Section: Related Workmentioning
confidence: 99%
“…Hierarchical topologies described in the previous section could avoid losses through energy-aware clusterization. Recent works studied fully decentralized FL based on peerto-peer aggregation to allow nearby devices with limited energy or weak signal strength to share their models [14]. Such process could be applied to F-MADRL, allowing distributed agents to gather and aggregate models from surrounding devices.…”
Section: B Optimizing Energy Efficiencymentioning
confidence: 99%
“…Reducing communication overheads through FL directly ties into energy savings. Given that every data exchange involves energy consumption, optimizing the FL process impacts the bandwidth and potentially contributes to reduced energy expenditures, a vital consideration for modern communication networks [15,16]. FL's capacity to minimize data transmission inherently reduces energy consumption, placing it at the forefront of strategies to develop energy-efficient communication networks.…”
Section: Introductionmentioning
confidence: 99%