Conventional Distributed/Decentrailzed/Federated PCA (43 papers)
Distributed (35 papers)
Z. Bai, R. Chan, F. Lu, "Principal Component Analysis for Distributed Data Sets with Updating" International Workshop on Advanced Parallel Processing Technologies, pages 471-483, 2005.
M. Livani, M. Abadi, "Distributed PCA-based anomaly detection in wireless sensor networks”, IEEE International Conference for Internet Technology and Secured Transactions, 2010.
L. Li, A. Scaglione, J. Manton, “Distributed principal subspace estimation in wireless sensor networks", IEEE Journal of Seleted Topics of Signal Processing, Volume 5, No. 4, pages 725–738, 2011.
Z. Meng, A. Wiesel, A. Hero, "Distributed principal component analysis on networks via directed graphical models", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2012, pages 2877-2880, 2012.
A. Aduroja, I. Schizas, V. Maroulas, "Distributed principal components analysis in sensor networks", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, pages 5850-5854, 2013.
M. Balcan, V. Kanchanapally, Y. Liang, D. Woodruff, "Improved Distributed Principal Component Analysis", Advances in neural information processing systems, 2014.
T. Elgamal, M. Hefeeda, "Analysis of PCA Algorithms in Distributed Environments", 2015.
N. An, S. Weber, "On the performance overhead tradeoff of distributed principal component analysis via data partitioning", Annual Conference on Information Science and Systems, CISS 2016, pages 578-583, 2016.
Q. Jiang, X. Yan, B. Huang, "Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference", IEEE Transactions on Industrial Electronics, Volume 63, No. 1, pages 377-386, 2016.
J. Zhu, Z. Ge, Z. Song, "Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes with Big Data", IEEE Transactions on Industrial Informatics, Volume 13, No. 4, pages 1877-1885, 2017.
S. Guan, J. Yin, "A Fast Distributed Principal Component Analysis with Variance Reduction”, IEEE International Symposium on Distributed Computing and Applications to Business, Engineering and Science, DCABES 2017, pages 11-14, 2017.
Y. Wang, Q. Jiang, J. Fu, "Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes", IEEE Access, Volume 5, pages 325-333, 2017.
V. Huroyan, G. Lerman, "Distributed robust subspace recovery", March 2018.
K. Chaudhuri, A. Sarwate, K. Sinha. "A near-optimal algorithm for differentially-private principal components", The Journal of Machine Learning Research, Volume 14, No.1, pages 2905–2943, 2013.
H. Imtiaz, A. Sarwate, "Differentially Private Distributed Principal Component Analysis", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, pages 2206-2210, 2018.
H. Imtiaz, A. Sarwate, “Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization” , IEEE Journal of Selected Topics in Signal Processing, December 2018.
S. Wang, J. Chang, "Differentially Private Principal Component Analysis Over Horizontally Partitioned Data", IEEE Conference on Dependable and Secure Computing, DSC 2018, 2018.
S. Wu, H. Wai, L. Li, A. Scaglione, "A Review of Distributed Algorithms for Principal Component Analysis", Proceedings of the IEEE, Volume 106, pages 1321-1340, 2018.
A. Gang, H. Raja, W. Bajwa, "Fast and Communication-efficient Distributed PCA”, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, pages 7450-7454, 2019.
L. Wang, "Research on Distributed Parallel Dimensionality Reduction Algorithm Based on PCA Algorithm”, IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, pages 1363-1367, 2019.
D. Tarzanagh, M. Faradonbeh, G. Michailidis, “Online Distributed Estimation of Principal Eigenspaces”, IEEE Data Science Workshop, DSW 2019, pages 27-31, 2019.
W. Bajwa, V. Cevher, D. Papailiopoulos, and A. Scaglione, “Machine learning from distributed, streaming data", IEEE Signal Processing Magazine, Volume 37, No. 3, pages 11-13, May 2020.
X. Wang, J. Chen, "Distributed Principal Component Analysis Based on Randomized Low-Rank Approximation”, IEEE International Conference on Signal Processing, Communications and Computing , ICSPCC 2020, pages 1-5, 2020.
Z. Yang, A. Gang, W. Bajwa, “Adversary-resilient distributed and decentralized statistical inference and machine learning: An overview of recent advances under the Byzantine threat model”, IEEE Signal Processing Magazine, Volume 37, No. 3, pages 146–159, 2020.
A. Gang, B. Xiang, W. Bajwa, “Distributed principal subspace analysis for partitioned big data: Algorithms, analysis, and implementation", IEEE Transactions on Signal and Information Processing over Networks, Volume 7, pages 699–715, 2021.
F. He, K. Lv, J. Yang, X. Huang, "One-Shot Distributed Algorithm for PCA With RBF Kernels", IEEE Signal Processing Letters, Volume 28, pages 1465-1469, 2021.
Z. Zhang, R. Wang, T. Li, L. Sun, V. Lau, K. Huang, "Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis", IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021, pages 601-605, 2021.
T. Adnan, M. Tanjim, M. Adnam “Fast, scalable and geo-distributed PCA for big data analytics”, Information Systems, Volume 98, May 2021.
Y. Cao, X. Yuan, Y. Wang, W. Gui, “Hierarchical hybrid distributed PCA for plant-wide monitoring of chemical processes”, Control Engineering Practice, Volume 111, June 2021.
A. Gang, W. Bajwa, “A linearly convergent algorithm for distributed principal component analysis", Signal Processing, Volume 193, 2022.
A. Gang, W. Bajwa, "FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis", IEEE Transactions on Signal Processing, Volume 70, pages 6080-6095, 2022
F. Andrade, M. Figueiredo, J. Xavier, “Distributed Picard iteration", Preprint, 2021.
F. Andrade, M. Figueiredo, J. Xavier, “Distributed Picard iteration: Application to distributed EM and distributed PCA", Preprint, 2021.
F. Andrade, M. Figueiredo, J. Xavier, “Distributed Banach-Picard Iteration: Application to Distributed EM and Distributed PCA”, Preprint, January 2022.
X. Wang, Y. Jiao, H. Wai, Y. Gu, “Incremental Aggregated Riemannian Gradient Method for Distributed PCA”, International Conference on Artificial Intelligence and Statistics, pages 7492-7510, 2023.
Decentralized (4 papers)
A. Scaglione, R. Pagliari, H. Krim, “The Decentralized Estimation of the Sample Covariance", Asilomar Conference on Signals, Systems and Computers, pages 1722-1726, 2008.
H. Ye, T. Zhang, “DeePCA: Decentralized Exact PCA with Linear Convergence Rate”, Journal of Machine Learning Research, Volume 22, No. 238, pages 1–27, 2021.
B. Xiao, Y. Li, B. Sun, C. Yang, K. Huang, H. Zhu, “Decentralized PCA Modeling based on Relevance and Redundancy Variable Selection and its Application to Large-Scale Dynamic Process Monitoring”, Process Safety and Environmental Protection, Volume 151, pages 85-100, July 2021.
S. Chen, A. Garcia, M. Hong, S. Shahrampour, “Decentralized Riemannian Gradient Descent on the Stiefel Manifold", Preprint, 2021.
Federated (5 papers)
A. Grammenos, R. Mendoza Smith, J. Crowcroft, C. Mascolo, “Federated Principal Component Analysis”, NeurIPS 2020, 2020.
D. Froelicher , H. Cho, M. Edupalli, J. Sousa, J. Bossuat, A. Pyrgelis, "Scalable and Privacy-Preserving Federated Principal Component Analysis", IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, 2023.
T. Nguyen, J. He, L. Le, W. Bao, N.Tran, "Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks", IEEE Conference on Computer Communications, INFOCOM 2023, pages 1-10, New York, USA, 2023.
A. Singh, N. Vaswani, “Byzantine-Resilient Federated PCA and Low Rank Matrix Recovery”, Preprint, 2023.
A. Singh, N. Vaswani, "Byzantine-Resilient Federated PCA and Low Rank Column-wise Sensing", IEEE Transactions on Information Theory, 2024.
Neural Networks (1 paper)
P. Li, S. Ankireddy, R. Zhao, H. Mahjoub, E. Pari, U. Topcu, S. Chinchali, H. Kim, “Task-aware Distributed Source Coding under Dynamic Bandwidth”, Neural Information Processing Systems, NeurIPS 2023, 2023.