Energy Efficient Federated Learning over Cooperative Relay-Assisted Wireless Networks

Zhang, Xinyue; Chen, Rui; Wang, Jingyi; Zhang, Huaqing; Pan, Miao

doi:10.1109/globecom48099.2022.10000664

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Performance evaluation indicated that the proposed FL-based scheme improves classification accuracy by approximately 7% compared to state-of-the-art CL approaches. In the same way, the authors of [39] and [40] also focus on EE maximization when considering FL schemes for relay-assisted B5G/6G IoT networks. The optimization goal of the proposed approach was to reduce IoT device energy consumption while jointly meeting wireless transmission latency and model training calculation time constraints during the FL training phase.…”

Section: Related Workmentioning

confidence: 99%

“…The optimization goal of the proposed approach was to reduce IoT device energy consumption while jointly meeting wireless transmission latency and model training calculation time constraints during the FL training phase. The key novelty of [40] was the integration of a weighted communication rate of all participating devices to maximize the convergence time for local model aggregation. To do so, the authors formed a maximum-weight independent problem that was solved approximately using graph theory techniques.…”

Section: Related Workmentioning

confidence: 99%

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A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

Bartsiokas,

Gkonis,

Kaklamani

et al. 2024

Electronics

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Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

Bartsiokas,

Gkonis,

Kaklamani

et al. 2024

Electronics

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show abstract

“…Therefore, adopting either data or model parallelism for collaborative EI execution may lead to communication overhead among participating RCDs. For edge DL training techniques such as federated learning [37], some of the studies [38], [39], [40], [41], [42], [43], [44], [45] consider relay-assisted communication to reduce communication costs and improve the network coverage of the system. Our goal is to apply this approach for edge inference scenarios where there is a data owner RCD that has the input data to be processed in an already-trained DL model and multiple collaborative RCDs to help it perform DL execution under a low latency requirement.…”

Section: Introductionmentioning

confidence: 99%

A Relay-Assisted Communication Scheme for Collaborative On-Device CNN Execution Considering Hybrid Parallelism

Kilcioglu,

Stupia,

Vandendorpe

2023

IEEE Access

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Deep learning (DL) has gained increasing prominence in latency-critical artificial intelligence (AI) applications. Due to the intensive computational requirements of these applications, cloud-centric approaches have been attempted to address this issue, but they result in intolerable latency, network congestion, and privacy concerns. An alternative concept called edge intelligence, which combines AI and edge computing, has been proposed to perform DL execution at the edge in multiple resource-constrained devices (RCDs) collaboratively. This paper proposes a relay-assisted, distributed, and collaborative on-device convolutional neural network (CNN) execution scheme for latency-critical applications. The system employs hybrid parallelism, combining both data and model parallelism, to optimize collaborative CNN execution on RCDs. A relay-assisted communication technique is used to reduce the input data size per RCD and avoid excessive point-to-point communication between the data owner RCD and collaborative RCDs. The proposed approach reduces communication overhead using two strategies: layer block formation and optimal filter assignment. These strategies are applied to multiple collaborative RCDs, considering their different computing capabilities and network conditions. Finally, a convex optimization problem is formulated to minimize the overall energy consumption by jointly optimizing the workload of each RCD in each layer and communication and computation parameters.

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Energy-Efficient Federated Learning-enabled Digital Twin in UAV-aided Vehicular Networks

Pham,

Le,

Pham

et al. 2023

2023 28th Asia Pacific Conference on Communications (APCC)

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Energy Efficient Federated Learning over Cooperative Relay-Assisted Wireless Networks

Cited by 5 publications

References 14 publications

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

A Relay-Assisted Communication Scheme for Collaborative On-Device CNN Execution Considering Hybrid Parallelism

Energy-Efficient Federated Learning-enabled Digital Twin in UAV-aided Vehicular Networks

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