Blar i NTNU Open på forfatter "Gogineni, Vinay Chakravarthi"
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Graph Kernel Recursive Least-Squares Algorithms
Gogineni, Vinay Chakravarthi; Naumova, Valeriya; Werner, Stefan; Huang, Yih-Fang (Chapter, 2022)This paper presents graph kernel adaptive filters that model nonlinear input-output relationships of streaming graph signals. To this end, we propose centralized and distributed graph kernel recursive least-squares (GKRLS) ... -
Improving the Performance of Multitask Diffusion APA via Controlled Inter-Cluster Cooperation
Gogineni, Vinay Chakravarthi; Chakraborty, Mrityunjoy (Peer reviewed; Journal article, 2020)In this paper, we consider the problem of estimating multiple parameter vectors over a sensor network in a multitasking framework and under temporally-correlated input conditions. For this, an efficient clustered multitask ... -
Kernel regression on graphs in random Fourier features space
Elias, Vitor; Gogineni, Vinay Chakravarthi; Martins, Wallace; Werner, Stefan (Chapter, 2021)This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient ... -
Kernel Regression over Graphs using Random Fourier Features
Meireles Elias, Vitor Rosa; Gogineni, Vinay Chakravarthi; Martins, Wallace; Werner, Stefan (Peer reviewed; Journal article, 2022)This paper proposes efficient batch-based and online strategies for kernel regression over graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal, whereas the target signal is defined ... -
Networked personalized federated learning using reinforcement learning
Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)Personalized federated learning enables every edge device or group of edge devices within the distributed network to learn a device- or cluster-specific model tailored to their local needs. Data scarcity, however, makes ... -
Novel VLSI Architecture for Fractional-Order Correntropy Adaptive Filtering Algorithm
Alex, Daney; Gogineni, Vinay Chakravarthi; Mula, Subrahmanyam; Werner, Stefan (Journal article; Peer reviewed, 2022)Conventional adaptive filters, which assume Gaussian distribution for signal and noise, exhibit significant performance degradation when operating in non-Gaussian environments. Recently proposed fractional-order adaptive ... -
Optimized Hyperspectral Anomaly Detectors: Improved Performance and Reduced Computational Complexity
Müller, Katinka (Master thesis, 2023)Hyperspektral anomalideteksjon (HAD) spiller en avgjørende rolle i ulike fjernmålingsapplikasjoner, inkludert miljøovervåking. Denne masteren fokuserer på å utvikle en HAD-modell som kan integreres i prosesseringen om bord ... -
Performance of clustered multitask diffusion LMS suffering from inter-node communication delays
Gogineni, Vinay Chakravarthi; Talebi, Sayed Pouria; Werner, Stefan (Peer reviewed; Journal article, 2021)This brief studies a clustered multitask diffusion least mean-square strategy that accounts for communication delays in the inter- and intra-cluster information exchanges. We conduct detailed performance analysis and ... -
Personalized graph federated learning with differential privacy
Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2023)This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining ... -
Personalized Online Federated Learning for IoT/CPS: Challenges and Future Directions
Gogineni, Vinay Chakravarthi; Werner, Stefan; Gauthier, Francois; Huang, Yih-Fang; Kuh, Anthony (Journal article, 2022)In recent years, federated learning (FL) has emerged as a powerful paradigm for distributed learning thanks to its privacy-preserving capabilities. With the use of FL, a network of edge devices can make intelligent decisions ... -
Recurrent Time-Varying Multi-Graph Convolutional Neural Network for Personalized Cervical Cancer Risk Prediction
Gogineni, Vinay Chakravarthi; Langberg, Geir Severin Rakh Elvatun; Naumova, Valeriya; Nygård, Jan Franz; Mari, Nygård,; Grasmair, Markus; Werner, Stefan (Chapter, 2021)Cervical cancer screening programs have reduced the incidence of cervical cancer, but suffer from over- and too infrequent screening as women’s risk of developing cervical cancer differs. Personalized risk prediction models ... -
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
Gauthier, Francois; Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Chapter, 2022)Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated ... -
Resource-efficient federated learning robust to communication errors
Lari, Ehsan; Gogineni, Vinay Chakravarthi; Arablouei, Reza; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)The effectiveness of federated learning (FL) in leveraging distributed datasets is highly contingent upon the accuracy of model exchanges between clients and servers. Communication errors caused by noisy links can negatively ... -
Towards a data-driven system for personalized cervical cancer risk stratification
Langberg, Geir Severin Rakh Elvatun; Nygård, Jan Franz; Gogineni, Vinay Chakravarthi; Nygård, Mari; Grasmair, Markus; Naumova, Valeriya (Peer reviewed; Journal article, 2022)Mass-screening programs for cervical cancer prevention in the Nordic countries have been effective in reducing cancer incidence and mortality at the population level. Women who have been regularly diagnosed with normal ...