Prototyping federated learning on edge computing systems

Yang, Jianlei; Duan, Yixiao; Qiao, Tong; Zhou, Huanyu; Wang, Jingyuan; Zhao, Weisheng

doi:10.1007/s11704-019-9237-3

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…The client selection scheme is used to select the clients that can contribute more to enhance the global model, which results in a reduction in the communication rounds [147,185,191]. Using asynchronous communication can enhance the global model performance by allowing the aggregation of the received model without waiting for all clients [146,171,211]. Select model update is a technique that uploads the trained model that can help model coverage and ignore irrelevant updates [157,189].…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…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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“…The results show that AFL has the same convergence rate as traditional FL while lowering communication requirements. By implementing the AFL scheme and conducting experiments on six Raspberry Pi 3B+ devices, the authors in [108] investigate the impact of heterogeneous devices. By conducting experiments on the MNIST dataset with non-iid data distribution, the results show that AFL performs better than traditional FL, especially when computing resources and input data sizes are clearly disparate.…”

Section: Non-independent and Identically Distributed Datamentioning

confidence: 99%

Asynchronous Federated Learning on Heterogeneous Devices: A Survey

Xiang

et al. 2021

Preprint

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Federated learning (FL) is experiencing a fast booming with the wave of distributed machine learning and ever-increasing privacy concerns. In the FL paradigm, global model aggregation is handled by a centralized aggregate server based on local updated gradients trained on local nodes, which mitigates privacy leakage caused by the collection of sensitive information. With the increased computing and communicating capabilities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models becomes a trend. The synchronous aggregation strategy in the classic FL paradigm cannot effectively use the resources, especially on heterogeneous devices, due to its waiting for straggler devices before aggregation in each training round. Furthermore, in real-world scenarios, the disparity of data dispersed on devices (i.e. data heterogeneity) downgrades the accuracy of models. As a result, many asynchronous FL (AFL) paradigms are presented in various application scenarios to improve efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing variants of AFL according to a novel classification mechanism, including device heterogeneity, data heterogeneity, privacy and security on heterogeneous devices, and applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated field. CCS Concepts: • Computer systems organization → Distributed architectures; • Computing methodologies → Learning paradigms; • Networks → Mobile networks.

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“…Thus, edge computing, which is on a smaller scale, has now become a new hot topic [7]. By extending data and computing to the edge, network delay no longer poses a problem and optimal decisions can be made promptly [8].…”

Section: Introductionmentioning

confidence: 99%

Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection

Liu

Zhang²,

Zhou³

et al. 2022

Electronics

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The algorithm of the clustering-based decision tree, which is a methodology of multimodal fusion, has made many achievements in many fields. However, it is not common in the field of transportation, especially in the application of automobile navigation. Meanwhile, the concept of Spatio-temporal data is now widely used. Therefore, we proposed a vehicle routing Spatio-temporal selection system based on a clustering-based decision tree. By screening and clustering Spatio-temporal data, which is a collection of individual point data based on historical driving data, we can identify the routes and many other features. Through the decision tree modeling of the state information of Spatio-temporal data, which includes the features of the historical data and route selection, we can obtain an optimal result, that is, the route selection made by the system. Moreover, all the above calculations and operations are done on the edge, which is different from the vast majority of current cloud computing vehicle navigation. We have also experimented with our system using real vehicle data. The experiments show that it can output path decision results for a given situation, which takes little time and is the same as the approximated case of networked navigation. The experiments yielded satisfactory results. Our system could save a lot of cloud computing power, which might change the current navigation systems.

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Prototyping federated learning on edge computing systems

Cited by 8 publications

References 2 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

Asynchronous Federated Learning on Heterogeneous Devices: A Survey

Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection

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