{"list":"other","total":55,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1405.4980","title":"Convex Optimization: Algorithms and Complexity","created":"2014-05-20","cited_by_count":575},{"id":"2003.10422","title":"A Unified Theory of Decentralized SGD with Changing Topology and Local Updates","created":"2020-03-23","cited_by_count":367},{"id":"1905.10018","title":"Momentum-Based Variance Reduction in Non-Convex SGD","created":"2019-05-24","cited_by_count":306},{"id":"1910.00189","title":"The Non-IID Data Quagmire of Decentralized Machine Learning","created":"2019-10-01","cited_by_count":305},{"id":"1807.01695","title":"SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator","created":"2018-07-04","cited_by_count":287},{"id":"1906.00331","title":"On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems","created":"2019-06-02","cited_by_count":270},{"id":"1404.6264","title":"EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization","created":"2014-04-24","cited_by_count":160},{"id":"2007.01154","title":"Federated Learning with Compression: Unified Analysis and Sharp Guarantees","created":"2020-07-02","cited_by_count":160},{"id":"1310.7063","title":"On the Convergence of Decentralized Gradient Descent","created":"2013-10-26","cited_by_count":159},{"id":"2002.02417","title":"Near-Optimal Algorithms for Minimax Optimization","created":"2020-02-05","cited_by_count":119},{"id":"1805.11454","title":"Distributed Stochastic Gradient Tracking Methods","created":"2018-05-25","cited_by_count":106},{"id":"2102.12660","title":"Distributionally Robust Federated Averaging","created":"2021-02-25","cited_by_count":104},{"id":"2006.08907","title":"Robust Federated Learning: The Case of Affine Distribution Shifts","created":"2020-06-16","cited_by_count":102},{"id":"2001.03724","title":"Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems","created":"2020-01-11","cited_by_count":77},{"id":"2202.03836","title":"An Improved Analysis of Gradient Tracking for Decentralized Machine Learning","created":"2022-02-08","cited_by_count":69},{"id":"2103.15888","title":"The Complexity of Nonconvex-Strongly-Concave Minimax Optimization","created":"2021-03-29","cited_by_count":45},{"id":"1910.12999","title":"A Decentralized Parallel Algorithm for Training Generative Adversarial Nets","created":"2019-10-28","cited_by_count":43},{"id":"2006.11773","title":"Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization","created":"2020-06-21","cited_by_count":43},{"id":"2102.13152","title":"Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency","created":"2021-02-25","cited_by_count":40},{"id":"2203.04850","title":"Federated Minimax Optimization: Improved Convergence Analyses and Algorithms","created":"2022-03-09","cited_by_count":37},{"id":"2301.01313","title":"Decentralized Gradient Tracking with Local Steps","created":"2023-01-03","cited_by_count":32},{"id":"1809.00710","title":"A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks","created":"2018-09-03","cited_by_count":27},{"id":"1812.03705","title":"Defending Against Universal Perturbations With Shared Adversarial Training","created":"2018-12-10","cited_by_count":25},{"id":"2008.10103","title":"Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning","created":"2020-08-23","cited_by_count":24},{"id":"2210.04757","title":"On the Performance of Gradient Tracking with Local Updates","created":"2022-10-10","cited_by_count":21},{"id":"2102.06333","title":"Efficient Algorithms for Federated Saddle Point Optimization","created":"2021-02-12","cited_by_count":20},{"id":"2005.10675","title":"An Optimal Algorithm for Decentralized Finite Sum Optimization","created":"2020-05-20","cited_by_count":19},{"id":"2301.04632","title":"Federated Learning under Heterogeneous and Correlated Client Availability","created":"2023-01-11","cited_by_count":18},{"id":"2008.07428","title":"Fast decentralized non-convex finite-sum optimization with recursive variance reduction","created":"2020-08-17","cited_by_count":17},{"id":"2110.01165","title":"DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization","created":"2021-10-04","cited_by_count":16},{"id":"2009.04373","title":"Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization","created":"2020-09-09","cited_by_count":13},{"id":"2206.02468","title":"An Optimal Transport Approach to Personalized Federated Learning","created":"2022-06-06","cited_by_count":12},{"id":"2212.02724","title":"Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems","created":"2022-12-06","cited_by_count":11},{"id":"2102.07758","title":"Decentralized Distributed Optimization for Saddle Point Problems","created":"2021-02-15","cited_by_count":10},{"id":"2206.01132","title":"A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning","created":"2022-06-02","cited_by_count":9},{"id":"2011.05082","title":"Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems","created":"2020-11-10","cited_by_count":8},{"id":"2302.04249","title":"Federated Minimax Optimization with Client Heterogeneity","created":"2023-02-08","cited_by_count":8},{"id":"2107.05957","title":"Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks","created":"2021-07-13","cited_by_count":7},{"id":"2302.03825","title":"Decentralized Riemannian Algorithm for Nonconvex Minimax Problems","created":"2023-02-08","cited_by_count":7},{"id":"2212.02387","title":"An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization","created":"2022-12-05","cited_by_count":6},{"id":"2304.02441","title":"Decentralized gradient descent maximization method for composite nonconvex strongly-concave minimax problems","created":"2023-04-05","cited_by_count":6},{"id":"2303.02532","title":"PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities","created":"2023-03-05","cited_by_count":5},{"id":"2303.14289","title":"Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization","created":"2023-03-24","cited_by_count":5},{"id":"2212.03095","title":"Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations","created":"2022-11-30","cited_by_count":4},{"id":"2307.09421","title":"Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization","created":"2023-07-18","cited_by_count":4},{"id":"2105.14216","title":"CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems","created":"2021-05-29","cited_by_count":2},{"id":"2106.10022","title":"Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Optimization","created":"2021-06-18","cited_by_count":2},{"id":"2307.07113","title":"Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems","created":"2023-07-14","cited_by_count":2},{"id":"2310.09356","title":"Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs","created":"2023-10-13","cited_by_count":2}],"next_cursor":null}