Federated Learning based Spatio-Temporal framework for real-time traffic prediction

Kaur, Gaganbir; Grewal, Surender K.; Jain, Aarti

doi:10.21203/rs.3.rs-2470634/v1

Cited by 3 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computation of the RMSE of the mean squared deviations between the predicted and actual weights is provided by Equation (6).…”

Section: Rmsementioning

confidence: 99%

“…The goal of this research is to investigate privacy-preserving strategies in FL by investigating FL frameworks, differential privacy, and cryptography techniques that balance data confidentiality and model correctness. 6 It is crucial to understand the larger implications for the direction machine learning is taking in order investigate ways to optimize FL. The combination of decentralized learning with emerging technologies like 5G networks and edge computing creates new opportunities for context-aware, real-time applications.…”

Section: Introductionmentioning

confidence: 99%

“…It is a difficult balancing act to maintain privacy while maximizing model performance, though. The goal of this research is to investigate privacy‐preserving strategies in FL by investigating FL frameworks, differential privacy, and cryptography techniques that balance data confidentiality and model correctness 6 . It is crucial to understand the larger implications for the direction machine learning is taking in order investigate ways to optimize FL.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

Lakshminarayanan,

Dhanasekaran,

Vinod Kumar

et al. 2024

Int J Communication

View full text Add to dashboard Cite

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

show abstract

“…The computation of the RMSE of the mean squared deviations between the predicted and actual weights is provided by Equation (6).…”

Section: Rmsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

Lakshminarayanan,

Dhanasekaran,

Vinod Kumar

et al. 2024

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In Graph Convolutional Recurrent Neural Networks, the choice of recurrent neural network includes using Gated Recurrent Unit or Long Short-Term Memory (e.g. [18][19][20][21][22][23][24]). This allows researchers to choose the appropriate recurrent neural network structure based on the characteristics and requirements of the task.…”

Section: Graph Convolutional Recurrent Neural Networkmentioning

confidence: 99%

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Zhang,

Zhou,

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper addresses the challenges of exponentially growing traffic in cellular networks by proposing a novel predictive model, HGCRN, which combines static graph convolutional recurrent neural network and meta-graph learning. The model is designed to effectively capture the complex spatio-temporal dependencies in network traffic, enhancing prediction accuracy and operational efficiency. By constructing graph adjacency matrices that go beyond mere geographical proximity, HGCRN offers a deeper understanding of the dynamic interactions within the network. Tested on real-world datasets from Telecom Italia and China Mobile, the model demonstrates significant improvements over traditional and state-of-the-art methods in terms of predictive accuracy and reliability.

show abstract

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Belal,

Ben Mokhtar,

Haddadi

et al. 2024

ACM Trans. Spatial Algorithms Syst.

View full text Add to dashboard Cite

Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly sensitive location data. However, there are unique challenges involved with transitioning existing spatial temporal models to federated learning. In this survey paper, we review the existing literature that has proposed FL-based models for predicting human mobility, traffic prediction, community detection, location-based recommendation systems, and other spatial-temporal tasks. We describe the metrics and datasets these works have been using and create a baseline of these approaches in comparison to the centralized settings. Finally, we discuss the challenges of applying spatial-temporal models in a decentralized setting and by highlighting the gaps in the literature we provide a road map and opportunities for the research community.

show abstract

Federated Learning based Spatio-Temporal framework for real-time traffic prediction

Cited by 3 publications

References 18 publications

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Contact Info

Product

Resources

About