arXiv subjects

Yuvraj Dutta

Publications and source records attributed to Yuvraj Dutta.

3 recordsLinked to original sources

arXiv2026

Bivariate inverse Gaussian degradation processes with shared random effects and an application to fatigue cracks

The inverse Gaussian (IG) process is a widely used model for univariate degradation data. For bivariate degradation data involving two performance characteristics (PCs), dependence is often introduced through an unobserved shared frailty factor combined with IG processes. Previous studies typically assume a specific frailty distribution, such as normal or gamma, although such choices are difficult to justify because the frailty is unobserved. This paper proposes a general IG GG framework for modeling bivariate degradation data with dependent PCs. Each degradation process is modeled using an IG process, while the shared frailty follows the generalized gamma (GG) family, which includes exponential, gamma, Weibull, and lognormal distributions as special cases. The proposed framework allows flexible selection of an appropriate frailty distribution within the GG family, leading to improved model fitting. Convenient parameter estimation procedures are developed and evaluated through simulation studies, demonstrating satisfactory performance. The proposed model is applied to fatigue crack data and compared with several existing frailty based and copula based models. Results show that the IG GG model provides a superior fit. System reliability estimation under the IG GG framework is also discussed.

stat.ME↗

arXiv2025

Federated Learning for Deforestation Detection: A Distributed Approach with Satellite Imagery

Accurate identification of deforestation from satellite images is essential in order to understand the geographical situation of an area. This paper introduces a new distributed approach to identify as well as locate deforestation across different clients using Federated Learning (FL). Federated Learning enables distributed network clients to collaboratively train a model while maintaining data privacy and security of the active users. In our framework, a client corresponds to an edge satellite center responsible for local data processing. Moreover, FL provides an advantage over centralized training method which requires combining data, thereby compromising with data security of the clients. Our framework leverages the FLOWER framework with RAY framework to execute the distributed learning workload. Furthermore, efficient client spawning is ensured by RAY as it can select definite amount of users to create an emulation environment. Our FL framework uses YOLOS-small (a Vision Transformer variant), Faster R-CNN with a ResNet50 backbone, and Faster R-CNN with a MobileNetV3 backbone models trained and tested on publicly available datasets. Our approach provides us a different view for image segmentation-based tasks on satellite imagery.

cs.CV↗

arXiv2025

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications

Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. We evaluate three aggregation algorithms - FedAvg, FedProx, and FedBN - across four time-series architectures: LSTM, CNN, CNN+LSTM, and Transformer, using five diverse real-world datasets. We systematically analyze the effects of client heterogeneity, cohort size, and history window length on prediction performance. Our results reveal key trade-offs among model complexities, convergence rates, and generalization. It is found that FedBN consistently delivers robust performance under non-IID conditions. On the other hand, LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores. Moreover, although Transformers converge in half the rounds of LSTM, they require longer history windows to achieve a high R2, indicating higher context dependence. LSTM is, therefore, found to achieve a favorable balance between accuracy, rounds, and temporal footprint. To validate the end-to-end applicability of the framework, we have integrated our FL-based predictors into a live adaptive streaming pipeline. It is seen that FedBN-based LSTM and Transformer models improve mean QoE scores by 11.7% and 11.4%, respectively, over FedAvg, while also reducing the variance. These findings offer actionable insights for building scalable, privacy-preserving, and edge-aware throughput prediction systems in next-generation wireless networks.

cs.DC↗