Refereed Journals
[J31] E. Lari, R. Arablouei, V. C. Gogineni, and S. Werner, “Resilience in online federated learning: Mitigating model-poisoning attacks via partial sharing,” IEEE Trans. Signal and Info. Process. Networks, 2025.
[J30] E. S. Nadimi, J-M. Braun, S-O. Benedicte, S. Khare, V. C. Gogineni, B-V. Victoria, and G. Baatrup, “Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway,” Scientific Reports, vol.15, no. 5960, 2025. Link to paper
[J29] M. S. Cheema, A. Chawla, V. C. Gogineni, P. S. Rossi, “Channel estimation in RIS-aided heterogeneous wireless Networks via federated learning,” IEEE Communications Lett., 2025. Link to paper
[J28] P. Ganjimala, V. C. Gogineni, and S. Mula, “Performance analysis of hammerstein block-oriented functional link adaptive filters,” IEEE Signal Process. Lett., vol. 31, pp. 2325-2329, 2024. Link to paper
[J27] M. S. Cheema, V. C. Gogineni, P. S. Rossi, and S. Werner, “Networked federated meta learning over extending graphs,” IEEE Internet Things J., 2024. Link to paper
[J26] S. Ramesh, K. Manna, V. C. Gogineni, S. Chattopadhyay, and S. Mahapatra, “Congestion-aware vertical link placement and application mapping onto three-dimensional network-on-chip architectures,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 43, no. 8, pp. 2249-2262, 2024. Link to paper
[J25] M. S. Cheema, M. Z. Sarwar, V. C. Gogineni, D. Cantero, and P. S. Rossi, “Computationally efficient structural health monitoring of bridges using graph signal processing,” IEEE Sensors J., vol. 24, no. 7, pp. 11895-11905, 2024. Link to paper
[J24] V. C. Gogineni, K. Müller, M. Orlandic, and S. Werner, “Light weight autoencoders for hyperspectral anomaly detection,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024. Link to paper
[J23] R. Mirzai, V. C. Gogineni, N. K. D. Venkategowda, and S. Werner, “Smoothing ADMM for sparse-penalized quantile regression with non-convex penalties,” IEEE Open J. Signal Processing, vol. 5, pp. 213-228, 2024. Link to paper
[J22] F. Gauthier, V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Personalized graph federated learning with differential privacy,” IEEE Trans. Signal and Info. Process. Networks, vol. 9, pp. 736-749, 2023. Link to paper
[J21] V. C. Gogineni, A. Moradi, N. K. D. Venkategowda and S. Werner, “Communication-efficient and privacy-aware distributed learning,” IEEE Trans. Signal and Info. Process., vol. 9, pp. 705-720, 2023. Link to paper
[J20] F. Gauthier, V. C. Gogineni, S. Werner, Y. Huang and A. Kuh, “Asynchronous online federated learning with reduced communication requirements,” IEEE Internet Things J., 2023. Link to paper
[J19] V. C. Gogineni, S. Ramesh, S. V. Mula and S. Werner, “Algorithm and architecture design of random Fourier features-based kernel adaptive filters,” IEEE Trans. Circuits. Syst. I: Regular Papers, vol. 70, no. 2, pp. 833-845, 2023. Link to paper
[J18] V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Communication-efficient online federated learning strategies for kernel regression,” IEEE Internet Things J., vol. 10, no. 5, pp. 4531-4544, 2023. Link to paper
[J17] V. C. Gogineni, S. Werner, F. Gauthier, Y. Huang and A. Kuh, “Personalized online federated learning for IoT/CPS: Challenges and future directions,” IEEE Internet Things Mag., vol. 5, no. 4, pp. 78-84, 2022. Link to paper
[J16] G. S. R. E. Langberg, J. Nygård, V. C. Gogineni, M. Nygård, M. Grasmair and V. Naumova, “Toward a data-driven system for personalized cervical cancer risk stratification,” Sci. Rep., vol. 12, no. 12083, 2022. Link to paper
[J15] A. Daney, V. C. Gogineni, S. V. Mula and S. Werner, “Novel VLSI architecture for fractional-order correntropy adaptive filtering algorithm,” IEEE Trans. VLSI Syst., vol. 30, no. 7, pp. 893-904, 2022. Link to paper
[J14] V. R. M. Elias, V. C. Gogineni, W. A. Martins and S. Werner, “Kernel regression over graphs using random Fourier features,” IEEE Trans. Signal Process., vol. 70, pp. 936-949, 2022. Link to paper
[J13] V. C. Gogineni, S. P. Talebi and S. Werner, “Performance of clustered multitask diffusion LMS suffering from inter-node communication delays,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 68, no. 7, pp. 2695-2699, 2021. Link to paper
[J12] V. R. M. Elias, V. C. Gogineni, W. A. Martins and S. Werner, “Adaptive graph filters in reproducing kernel Hilbert spaces: Design and performance analysis,” IEEE Trans. Signal Inf. Process. Networks, vol. 7, pp. 62-74, 2021. Link to paper
[J11] V. C. Gogineni, S. P. Talebi, S. Werner and D. P. Mandic, “Fractional-order correntropy adaptive filters for distributed processing of $\alpha$-stable signals,” IEEE Signal Process. Lett., vol. 27, pp. 1884-1888, 2020. Link to paper
[J10] V. C. Gogineni, S. P. Talebi, S. Werner and D. P. Mandic, “Fractional-order correntropy filters for tracking dynamic systems in $\alpha$-stable environments,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 67, no. 12, pp. 3557-3561, 2020. Link to paper
[J9] V. C. Gogineni and M. Chakraborty, “Improving the performance of multitask diffusion APA via controlled inter-cluster cooperation,” IEEE Trans. Circuits Syst. I: Regular Papers, vol. 67, no. 3, pp. 903-912, 2020.Link to paper
[J8] S. Mula, V. C. Gogineni and A. S. Dhar, “Robust proportionate adaptive filter architectures under impulsive noise,” IEEE Trans. VLSI Syst., vol. 27, no. 5, pp. 1223-1227, 2019. Link to paper
[J7] V. C. Gogineni, B. K. Das and M. Chakraborty, “An adaptive convex combination of APA and ZA-APA for identifying systems having variable sparsity and correlated input,” Digital Signal Process., vol. 82, pp. 118-132, 2018. Link to paper
[J6] V. C. Gogineni and S. Mula, “Logarithmic cost based constrained adaptive filtering algorithms for sensor array beamforming,” IEEE Sensors J., vol. 18, no. 14, pp. 5897-5905, 2018. Link to paper
[J5] V. C. Gogineni and S. Mula, “Improved proportionate-type sparse adaptive filtering under maximum correntropy criterion in impulsive noise environments,” Digital Signal Process., vol. 79, pp. 190-198, 2018. Link to paper
[J4] S. Mula, V. C. Gogineni and A. S. Dhar, “Algorithm and VLSI architecture design of proportionate-type LMS adaptive filters for sparse system identification,” IEEE Trans. VLSI Syst., vol. 26, no. 9, pp. 1750-1762, 2018. Link to paper
[J3] B. K. Das, V. C. Gogineni and M. Chakraborty, “A convex combination of NLMS and ZA-NLMS for identifying systems with variable sparsity,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 64, no. 9, pp. 1112 - 1116, 2017. Link to paper
[J2] S. Mula, V. C. Gogineni and A. S. Dhar, “Algorithm and architecture design of adaptive filters with error non-linearities,” IEEE Trans. VLSI Syst., vol. 25, no. 9, pp. 2588-2601, 2017. Link to paper
[J1] B. K. N. Srinivasarao, V. C. Gogineni, S. Mula, and I. Chakrabarti, “A novel framework for compressed sensing based scalable video coding,” Signal Process.: Image Comm., vol. 57, pp. 183 - 196, 2017. Link to paper
(Under Communication)
[J32] V. C. Gogineni, E. Nadimi, “Efficient knowledge deletion from trained models through layer-wise partial machine unlearning,” Journal of Machine Learning Research, 2024 (Revised and Submitted). Link to paper
[J33] V. C. Gogineni, J-M Braun, B. S. Olesen and, G. Baatrup and E. Nadimi, “Enhancing polyp characterization in colon capsule endoscopy using ResNet9-KAN,” Knowledge-based Systems, 2024 (Submitted). Link to paper
[J34] M. S. Cheema, A. Chawla, V. C. Gogineni, P. S. Rossi, “Region-specific reliable channel estimation in RIS-enabled wireless communications via clustered federated learning,” IEEE Internet of Things J., 2024 (Submitted).
[J35] E. Lari, V. C. Gogineni, R. Arablouei and S. Werner, “Noise-robust and resource-efficient ADMM-based federated learning,” IEEE Open J. Signal Processing, 2024 (Submitted). Link to paper
[J36] M. Parsayan, P. F. Høilund-Carlsen, V. C. Gogineni, and S. Andalib, “Classification of Alzheimer’s disease, mild cognitive impairment, and healthy subjects using a multimodal dataset and multi-class machine learning algorithms,” 2024 (Submitted).
[J37] M. Parsayan, P. F. Høilund-Carlsen, V. C. Gogineni, and S. Andalib, “Impact of reference region selection on SUVR values in FDG-PET imaging of Alzheimer’s disease,” Neuroscience, 2024 (Submitted).
Conference Proceedings
[C22] E. Lari, V. C. Gogineni, R. Arablouei, and S. Werner, “On the resilience of online federated learning to model poisoning attacks through partial sharing,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2024. Link to paper
[C21] E. Lari, V. C. Gogineni, R. Arablouei, and S. Werner, “Continual local updates for federated learning with enhanced robustness to link noise,” in Proc. IEEE Int. Conf. Asia Pacific Signal and Info. Process. Assoc., 2023. Link to paper
[C20] K. Müller, V. C. Gogineni, M. Orlandic, and S. Werner, “Autoencoder-based hyperspectral anomaly detection using kernel principal component pre-processing,” in Proc. European Conf. Signal Process., 2023. Link to paper
[C19] E. Lari, V. C. Gogineni, R. Arablouei, and S. Werner, “Resource-efficient federated learning robust to communication errors,” in Proc. IEEE Int. Workshop Statistical Signal Process., 2023. Link to paper
[C18] R. Mirzai, V. C. Gogineni, N. K. D. Venkategowda, and S. Werner, “Distributed quantile regression with non-convex sparse penalties,” in Proc. IEEE Int. Workshop Statistical Signal Process., 2023. Link to paper
[C17] F. Gauthier, V. C. Gogineni, and S. Werner, “Personalized networked federated learning using reinforcement learning,” in Proc. IEEE Int. Conf. Commun., 2023. Link to paper
[C16] F. Gauthier, V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Clustered graph federated personalized learning,” in Proc. Asilomar Conf. Signals, Systems, and Computers, 2022, pp. 744-748.
[C15] R. Mirzai, V. C. Gogineni, N. K. D. Venkategowda, and S. Werner, “Dynamic graph topology learning using ADMM with non-convex penalties,” in Proc. European Conf. Signal Process., 2022, pp. 682-686. Link to paper
[C14] R. Mirzai, N. K. D. Venkategowda, V. C. Gogineni, and S. Werner, “Proximal ADMM for sparse-penalized quantile regression,” in Proc. European Conf. Signal Process., 2022, pp. 2046-2050. Link to paper
[C13] V. C. Gogineni, A. Moradi, N. K. D. Venkategowda, and S. Werner, “Energy-efficient and privacy-aware distributed LMS,” in Proc. Int. Conf. Info. Fusion., 2022, pp. 1-6. Link to paper
[C12] V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Decentralized graph federated multitask learning for streaming data,” in Proc. Annual Conf. Info. Sci. and Syst., 2022, pp. 101-106. Link to paper
[C11] F. Gauthier, V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Resource-aware asynchronous online federated learning for nonlinear regression,” in Proc. IEEE Int. Conf. Commun., 2022, pp. 2828-2833. Link to paper
[C10] V. C. Gogineni, S. Werner, Y. Huang, and A. Kuh, “Communication-efficient online federated learning framework for nonlinear regression,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2022, pp. 5228-5232. Link to paper
[C9] V. C. Gogineni, V. Naumova, S. Werner, and Y-F. Huang, “Graph kernel recursive least-squares algorithms,” in Proc. IEEE Int. Conf. Asia Pacific Signal and Info. Process. Assoc., Tokyo, 2021, pp. 2072-2076. (Received Best Paper Award). Link to paper
[C8] V. C. Gogineni, G. S. R. E. Langberg, V. Naumova, J. Nygård, M. Nygård, M. Grasmair, and S. Werner, “Recurrent time-varying multi-graph convolutional neural networks for personalized cervical cancer risk prediction,” in Proc. Asilomar Conf. Signals, Systems, and Computers, 2021. Link to paper
[C7] V. C. Gogineni, G. S. R. E. Langberg, V. Naumova, J. Nygård, M. Nygård, M. Grasmair, and S. Werner, “Data-driven personalized cervical cancer risk prediction: A graph-perspective,” in Proc. IEEE Int. Workshop Statistical Signal Process., 2021, pp. 46-50. Link to paper
[C6] V. R. M. Elias, V. C. Gogineni, W. A. Martins, and S. Werner, “Kernel regression on graphs using random Fourier features,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2021, pp. 5235-5239. Link to paper
[C5] V. C. Gogineni, V. R. M. Elias, W. A. Martins, and S. Werner, “Graph diffusion kernel LMS using random Fourier features,” in Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 2020, pp. 1528-1532. Link to paper
[C4] V. C. Gogineni and M. Chakraborty, “Partial diffusion affine projection algorithm over clustered multitask networks,” in Proc. IEEE Int. Symp. on Circuits and Syst., May 2019, pp. 1-5.
[C3] V. C. Gogineni and M. Chakraborty, “Diffusion affine projection algorithm for multitask networks,” in Proc. IEEE Int. Conf. Asia Pacific Signal and Info. Process. Assoc., Nov. 2018, pp. 201-206.
[C2] V. C. Gogineni, S. Mula, and M. Chakraborty, “Performance analysis of proportionate-type LMS algorithms,” in Proc. IEEE Int. Conf. Signal Process.: Algo., Architec., Arrange., and Appli., Sep. 2016, pp. 177-181.
[C1] V. C. Gogineni, R. L. Das, and M. Chakraborty, “Proportionate-type hard thresholding adaptive filter for sparse system identification,” in Proc. IEEE Int. Conf. Asia Pacific Signal and Info. Process. Assoc., Dec. 2014, pp. 1-6.