HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning

Rango, Floriano De; Guerrieri, Antonio; Raimondo, Pierfrancesco; Spezzano, Giandomenico

doi:10.1016/j.pmcj.2023.101804

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…FedAvg [11] central FedAsync [12] central POWER-OF-CHOICE Strategy [14] central ✓ FedCS [15] central ✓ HFL [17][18][19] ✓ D2D-assisted hierarchical FL [22] D2D ✓ SD-FEEL [23] Edge Server TT-HF [24] D2D P2P FL [26] P2P FedPO D2D ✓ ✓…”

Section: Methods Communication Methods Hierarchical Architecture Clie...mentioning

confidence: 99%

“…FL mechanisms with various structures have been proposed. In the hierarchical FL (HFL) mechanism proposed in [ 17 , 18 , 19 ], a client and server communicated through an intermediate medium rather than a direct communication structure. In [ 19 ], the hierarchical edge federated learning (HED-FL) model enhances traditional FL with a multi-layered edge node architecture for energy-efficient learning.…”

Section: Related Workmentioning

confidence: 99%

“…In the hierarchical FL (HFL) mechanism proposed in [ 17 , 18 , 19 ], a client and server communicated through an intermediate medium rather than a direct communication structure. In [ 19 ], the hierarchical edge federated learning (HED-FL) model enhances traditional FL with a multi-layered edge node architecture for energy-efficient learning. Two heuristic methods were also introduced to assess the effects of static and dynamic round execution across these layers.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Methods Communication Methods Hierarchical Architecture Clie...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Jung,

Ko,

Lim

2024

Sensors

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“…The concept of FL involves using multiple devices to train a model, without having to share raw data with a central server [3,152]. Methods like this are beneficial when sensitive data, such as financial or healthcare records, cannot be shared.…”

Section: Federated Learningmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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Occupancy Prediction in Buildings: State of the Art and Future Directions

Khan,

Greco,

Guerrieri

et al. 2023

Internet of Things

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HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning

Cited by 11 publications

References 14 publications

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

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

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Occupancy Prediction in Buildings: State of the Art and Future Directions

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