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Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models.

, , , , , , and . IEEE BigData, page 1214-1225. IEEE, (2021)

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Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments., , and . Proceedings of the IEEE, 104 (1): 58-92 (2016)Semi-supervised Eigenvectors for Locally-biased Learning., and . NIPS, page 2537-2545. (2012)Randomized Algorithms for Matrices and Massive Data Sets., and . VLDB, page 1269. ACM, (2006)Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition., , and . SIAM J. Comput., 36 (1): 184-206 (2006)Fast Randomized Kernel Ridge Regression with Statistical Guarantees., and . NIPS, page 775-783. (2015)A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark., , , , , , , , , and 5 other author(s). IPDPS Workshops, page 1403-1412. IEEE Computer Society, (2016)Tree decompositions and social graphs., , and . Internet Mathematics, 12 (5): 315-361 (2016)Scalable Kernel K-Means Clustering with Nyström Approximation: Relative-Error Bounds., , and . J. Mach. Learn. Res., (2019)Approximating Higher-Order Distances Using Random Projections., , and . UAI, page 312-321. AUAI Press, (2010)Residual Networks as Nonlinear Systems: Stability Analysis using Linearization., , , and . CoRR, (2019)