Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

Zhai, Shaolei; Jin, Xin; Wei, Ling; Luo, Hongxuan; Cao, Min

doi:10.1109/access.2021.3050172

Cited by 25 publications

(9 citation statements)

References 25 publications

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“…2021 [20] Addresses the dynamic FL problem in a power grid mobile edge computing setting by proposing a delay deadline constrained FL framework and formulating a dynamic client selection problem, with two online client selection algorithms proposed to optimize utility in the learning framework.…”

Section: Pseudo Label-driven Federated Learningmentioning

confidence: 99%

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Mirdita

Bello

Refaey

et al. 2023

IEEE Open J. Commun. Soc.

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Advanced Indoor Positioning Systems (IPS) based on Received Signal Strength (RSS) fingerprints have been paramount in 6G network research and commercial exploitation due to their costeffectiveness and simplicity. Despite their popularity, the advent of 6G has prompted a shift towards exploring Deep Learning algorithms to further enhance their performance and precision. Deep Learning research typically demands large datasets, leading to reliance on data augmentation and crowdsourcing techniques for data collection. However, the traditional centralization of data in crowdsourcing poses privacy risks, and here is where Federated Learning (FL) comes into play. In light of this, our study introduces FL to bridge this divide in a decentralized way, eliminating the need for servers to acquire labeled data directly from users. This approach aims to minimize localization error in RSS fingerprints, preserve user privacy, and reduce system latency, all key goals for 6G networks. Moreover, we explore the use of power transmission techniques to further decrease the latency in the FL system. Our simulation outcomes confirm the superiority of FL over traditional Stochastic Gradient Descent (SGD) methods considering critical evaluation metrics like localization error and global loss, paving the way for efficient 6G implementation.

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Section: Pseudo Label-driven Federated Learningmentioning

confidence: 99%

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Mirdita

Bello

Refaey

et al. 2023

IEEE Open J. Commun. Soc.

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“…Authors in [5] proposed a client selection for the FL algorithm, named Client Selection Federated Averaging (CSFedAvg), to mitigate the biases in model training caused by non-independent identically distributed clients. The work in [6] presented a dynamic client selection scheme in a power grid mobile edge computing environment for the FL problem. In [7] an algorithm based on reinforcement learning was proposed for client selection to minimize the energy consumption and the training delay such that users are encouraged to participate in the FL process.…”

Section: A Related Workmentioning

confidence: 99%

Communication-Efficient Federated Learning for Hybrid VLC/RF Indoor Systems

et al. 2022

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Federated Learning (FL) enables smart devices to collaboratively train Machine Learning (ML) models in a distributed manner without sharing their private data with a central server. However, the disparity between the communication and computation capabilities, and the heterogeneity of local datasets of smart devices degrades the performance of FL in terms of latency and accuracy. To mitigate this effect, we address the problems of device selection and resource allocation in an indoor environment where multiple smart devices participate in the FL process. To further reduce the communication latency, we use Visible Light Communication (VLC) for the downlink transmission while a Radio Frequency (RF) access point supports the uplink transmission in the proposed system. Accordingly, we formulate a multi-objective optimization problem for joint device selection and resource allocation in a hybrid VLC/RF system. Then, using the weight methods, the problem is converted to a single-objective optimization which is solved by incrementally selecting devices in each iteration. The embedded device selection scheme in the proposed algorithm is based on the significance of candidate devices' local gradients and their alignment with the global tendency in order to intelligently prioritize the candidates in the training procedure. Simulation results show that the joint device selection and resource allocation scheme improves the accuracy of the ML model and reduces the average delay in presence of both system and data heterogeneity. Additionally, the proposed hybrid VLC/RF system decreases the latency of the FL process in the downlink mode compared to conventional RF systems.INDEX TERMS Visible light communication, federated learning, device selection, resource allocation, internet of things.

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“…Artificial neural networks based ML algorithms are trained on the consumer data using FL approach for big data analytics. In a similar work, Zhai et al [53] proposed an FL framework, constrained by delay deadline that overcomes long delays due to training of big data generated in smart grids.…”

Section: Federated Learning For Big Data Analyticsmentioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

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Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

Cited by 25 publications

References 25 publications

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Communication-Efficient Federated Learning for Hybrid VLC/RF Indoor Systems

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

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