Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions

Duan, Qiang; Huang, Jun; Hu, Shijing; Deng, Ruijun; Lu, Zhihui; Yu, Shui

doi:10.1109/comst.2023.3316615

Cited by 22 publications

(6 citation statements)

References 243 publications

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“…To address the heterogeneity of data and device heterogeneity, clustered FL has emerged. In clustered FL, server pair clients based on geographical location, task similarity, data distribution similarity, and resource similarity form clusters [30]. Reasonable grouping and pairing can reduce the differences in system functionality and dataset characteristics among clients within the same group.…”

Section: Cluster Pairingmentioning

confidence: 99%

Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing

Sha,

Sun,

Liu

et al. 2024

Electronics

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Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client–edge model aggregation with synchronous edge–cloud model aggregation. By leveraging the clients’ idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients’ local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures.

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Section: Cluster Pairingmentioning

confidence: 99%

Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing

Sha,

Sun,

Liu

et al. 2024

Electronics

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“…Furthermore, the integration of FL with MEC will be a pivotal step towards achieving ubiquitous intelligence in 6G networks. This combination will enable more efficient utilization of the vast amounts of data generated by devices through MEC [ 6 ]. Notably, in FL, the size of model parameters updated by training local devices, which may be billions in number, can reach tens of megabytes [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments

Jung,

Ko,

Lim

2024

Sensors

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Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, the parameter server (PS), which aggregates the local models that are trained using local user data, is typically far from users. The large distance may burden the path links between the PS and local nodes, thereby increasing the consumption of the network and computing resources. Second, user device resources are limited, but this aspect is not considered in the training of the local model and transmission of the model parameters. Third, the PS-side links tend to become highly loaded as the number of participating clients increases. The links become congested owing to the large size of model parameters. In this study, we propose a resource-efficient FL scheme. We follow the Pareto optimality concept with the biased client selection to limit client participation, thereby ensuring efficient resource consumption and rapid model convergence. In addition, we propose a hierarchical structure with location-based clustering for device-to-device communication using k-means clustering. Simulation results show that with prate at 0.75, the proposed scheme effectively reduced transmitted and received network traffic by 75.89% and 78.77%, respectively, compared to the FedAvg method. It also achieves faster model convergence compared to other FL mechanisms, such as FedAvg and D2D-FedAvg.

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“…This is crucial for FL due to the participation of a large and diverse set of devices in the training process [8]. Furthermore, wireless networks provide connectivity in remote locations or for mobile devices, enriching the training dataset with a wider range of data and potentially leading to more generalizable models [9]. Additionally, wireless communication protocols can be optimized for low power consumption, critical for battery-powered IoT devices, minimizing the burden on device batteries [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Dang,

Shin

2024

Electronics

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Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

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Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions

Cited by 22 publications

References 243 publications

Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing

Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing

Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

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