{"list":"other","total":57,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"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":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"1702.07476","title":"Renyi Differential Privacy","created":"2017-02-24","cited_by_count":650},{"id":"1805.09767","title":"Local SGD Converges Fast and Communicates Little","created":"2018-05-24","cited_by_count":605},{"id":"2111.04263","title":"Federated Learning Based on Dynamic Regularization","created":"2021-11-08","cited_by_count":562},{"id":"1509.01240","title":"Train faster, generalize better: Stability of stochastic gradient descent","created":"2015-09-03","cited_by_count":552},{"id":"1911.00222","title":"Federated Learning with Differential Privacy: Algorithms and Performance Analysis","created":"2019-11-01","cited_by_count":536},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"1909.13014","title":"FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization","created":"2019-09-28","cited_by_count":356},{"id":"1605.02065","title":"Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds","created":"2016-05-06","cited_by_count":346},{"id":"1311.0776","title":"The Composition Theorem for Differential Privacy","created":"2013-11-04","cited_by_count":297},{"id":"1808.07217","title":"Don't Use Large Mini-Batches, Use Local SGD","created":"2018-08-22","cited_by_count":202},{"id":"2002.07839","title":"Is Local SGD Better than Minibatch SGD?","created":"2020-02-18","cited_by_count":151},{"id":"2005.11418","title":"FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data","created":"2020-05-22","cited_by_count":144},{"id":"2202.09357","title":"ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!","created":"2022-02-18","cited_by_count":134},{"id":"2111.14213","title":"Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning","created":"2021-11-28","cited_by_count":114},{"id":"2006.03262","title":"UVeQFed: Universal Vector Quantization for Federated Learning","created":"2020-06-05","cited_by_count":107},{"id":"2306.11867","title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration","created":"2023-06-20","cited_by_count":97},{"id":"2011.02828","title":"Local SGD: Unified Theory and New Efficient Methods","created":"2020-11-03","cited_by_count":88},{"id":"1910.03197","title":"Accelerating Federated Learning via Momentum Gradient Descent","created":"2019-10-08","cited_by_count":76},{"id":"2111.09278","title":"Differentially Private Federated Learning on Heterogeneous Data","created":"2021-11-17","cited_by_count":74},{"id":"2206.00187","title":"DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training","created":"2022-06-01","cited_by_count":74},{"id":"2102.05855","title":"Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent","created":"2021-02-11","cited_by_count":73},{"id":"2207.14130","title":"FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning","created":"2022-07-28","cited_by_count":64},{"id":"2205.13710","title":"Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss","created":"2022-05-27","cited_by_count":61},{"id":"2302.10429","title":"FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy","created":"2023-02-21","cited_by_count":49},{"id":"2012.04061","title":"Faster Non-Convex Federated Learning via Global and Local Momentum","created":"2020-12-07","cited_by_count":48},{"id":"2203.05363","title":"Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)","created":"2022-03-10","cited_by_count":48},{"id":"1908.02997","title":"Local Differential Privacy for Deep Learning","created":"2019-08-08","cited_by_count":46},{"id":"2305.11584","title":"Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape","created":"2023-05-19","cited_by_count":46},{"id":"2106.10435","title":"STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning","created":"2021-06-19","cited_by_count":43},{"id":"2102.04737","title":"Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication","created":"2021-02-09","cited_by_count":39},{"id":"2203.03106","title":"Differentially Private Federated Learning with Local Regularization and Sparsification","created":"2022-03-07","cited_by_count":36},{"id":"2203.15104","title":"FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation","created":"2022-03-28","cited_by_count":36},{"id":"2202.07178","title":"Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy","created":"2022-02-15","cited_by_count":33},{"id":"2207.04338","title":"Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning","created":"2022-07-09","cited_by_count":33},{"id":"2303.11242","title":"Make Landscape Flatter in Differentially Private Federated Learning","created":"2023-03-20","cited_by_count":33},{"id":"2102.11158","title":"Federated $f$-Differential Privacy","created":"2021-02-22","cited_by_count":29},{"id":"2203.06735","title":"Private Non-Convex Federated Learning Without a Trusted Server","created":"2022-03-13","cited_by_count":26},{"id":"2308.00522","title":"Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup","created":"2023-07-30","cited_by_count":23},{"id":"2007.00914","title":"Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy","created":"2020-07-02","cited_by_count":22},{"id":"2212.14370","title":"Can 5th Generation Local Training Methods Support Client Sampling? Yes!","created":"2022-12-29","cited_by_count":22},{"id":"2306.05706","title":"Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization","created":"2023-06-09","cited_by_count":21},{"id":"2109.09142","title":"Decentralized Wireless Federated Learning with Differential Privacy","created":"2021-09-19","cited_by_count":18},{"id":"2302.12448","title":"Subspace based Federated Unlearning","created":"2023-02-24","cited_by_count":17},{"id":"2303.08678","title":"Visual Prompt Based Personalized Federated Learning","created":"2023-03-15","cited_by_count":16},{"id":"2302.11051","title":"Fusion of Global and Local Knowledge for Personalized Federated Learning","created":"2023-02-21","cited_by_count":14},{"id":"2106.15905","title":"Faithful Edge Federated Learning: Scalability and Privacy","created":"2021-06-30","cited_by_count":13},{"id":"2312.06989","title":"Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks","created":"2023-12-12","cited_by_count":13},{"id":"2403.00278","title":"Shifted Interpolation for Differential Privacy","created":"2024-03-01","cited_by_count":13},{"id":"2302.05726","title":"Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach","created":"2023-02-11","cited_by_count":11},{"id":"2102.00880","title":"Decentralized Federated Learning Preserves Model and Data Privacy","created":"2021-02-01","cited_by_count":8},{"id":"2403.17572","title":"Enhancing Privacy in Federated Learning through Local Training","created":"2024-03-26","cited_by_count":3}],"next_cursor":null}