Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

Huang, Wuwei; Yang, Yang; Chen, Mingzhe; Liu, Chuanhong; Feng, Chunyan; Poor, H. Vincent

doi:10.3390/e23111413

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Quantization [142][143][144][145]148,152,156,158,168,169,174,176,183,184,188,[191][192][193]198,199] Sparsification [140,141,151,153,155,165,174,186,200,202,204] Client Selection [147,166,172,185,191,198,207] Asynchronous [146,171,190,203,211] Two-Level Aggregation [164,175,180,182,185] Select Model Updates [149,157,…”

Section: Techniques Studies Referencedmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client’s limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.

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Section: Techniques Studies Referencedmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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“…These conditions distort the VLC optical beam, causing amplitude and phase fluctuations along with optical losses. The level of loss of the VLC signal is higher than the losses suffered by the RF network, as the VLC signal is very sensitive to atmospheric conditions such as sun, rain, and fog [136]. There are several factors (scattering, absorption, nonalignment, and turbulence) that can cause signal loss in both the VLC and RF networks, which complicate the design of the hybrid communication channel model [137].…”

Section: Hybrid Vlc/rf Network In Outdoor Scenarios With Harsh Condit...mentioning

confidence: 99%

A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions

Bravo Alvarez,

Montejo-Sánchez,

Rodríguez-López

et al. 2023

Sensors

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The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

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“…of devices & energy × RF [17] Convergence time & training loss RF [18] Alignment with global tendency × RF [19] No. of samples VLC/RF [20] No. There has been limited work on the application of VLC indoor systems for federated learning.…”

Section: Workmentioning

confidence: 99%

“…The final user selection and bandwidth allocation were obtained by iteratively solving the two sub-problems. The proposed algorithm in [20] first used a model compression method to reduce the size of the FL model in a hybrid VLC/RF system. Then, similar to [19], it solved the user selection and bandwidth allocation problems as two separate problems.…”

Section: 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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Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

Cited by 9 publications

References 22 publications

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions

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

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