{"list":"other","total":63,"rows":[{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"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":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"2007.07481","title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","created":"2020-07-15","cited_by_count":906},{"id":"2012.04221","title":"Ditto: Fair and Robust Federated Learning Through Personalization","created":"2020-12-08","cited_by_count":672},{"id":"2006.08848","title":"Personalized Federated Learning with Moreau Envelopes","created":"2020-06-16","cited_by_count":618},{"id":"1805.09767","title":"Local SGD Converges Fast and Communicates Little","created":"2018-05-24","cited_by_count":605},{"id":"1405.4980","title":"Convex Optimization: Algorithms and Complexity","created":"2014-05-20","cited_by_count":575},{"id":"1811.03604","title":"Federated Learning for Mobile Keyboard Prediction","created":"2018-11-08","cited_by_count":572},{"id":"2102.07078","title":"Exploiting Shared Representations for Personalized Federated Learning","created":"2021-02-14","cited_by_count":541},{"id":"1911.00222","title":"Federated Learning with Differential Privacy: Algorithms and Performance Analysis","created":"2019-11-01","cited_by_count":536},{"id":"1909.04630","title":"Meta-Learning with Implicit Gradients","created":"2019-09-10","cited_by_count":525},{"id":"2003.13461","title":"Adaptive Personalized Federated Learning","created":"2020-03-30","cited_by_count":358},{"id":"2002.10619","title":"Three Approaches for Personalization with Applications to Federated Learning","created":"2020-02-25","cited_by_count":325},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315},{"id":"2007.03797","title":"Personalized Cross-Silo Federated Learning on Non-IID Data","created":"2020-07-07","cited_by_count":283},{"id":"1807.06629","title":"Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning","created":"2018-07-17","cited_by_count":278},{"id":"1903.03934","title":"Asynchronous Federated Optimization","created":"2019-03-10","cited_by_count":267},{"id":"1909.04746","title":"Tighter Theory for Local SGD on Identical and Heterogeneous Data","created":"2019-09-10","cited_by_count":266},{"id":"1810.03548","title":"Meta-Learning: A Survey","created":"2018-10-08","cited_by_count":256},{"id":"2103.04628","title":"Personalized Federated Learning using Hypernetworks","created":"2021-03-08","cited_by_count":212},{"id":"2012.08565","title":"Personalized Federated Learning with First Order Model Optimization","created":"2020-12-15","cited_by_count":201},{"id":"1511.03575","title":"Federated Optimization:Distributed Optimization Beyond the Datacenter","created":"2015-11-11","cited_by_count":195},{"id":"2106.06639","title":"Federated Learning with Buffered Asynchronous Aggregation","created":"2021-06-11","cited_by_count":190},{"id":"2003.08673","title":"Survey of Personalization Techniques for Federated Learning","created":"2020-03-19","cited_by_count":145},{"id":"1902.08438","title":"Online Meta-Learning","created":"2019-02-22","cited_by_count":137},{"id":"1908.10400","title":"On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms","created":"2019-08-27","cited_by_count":132},{"id":"2102.06387","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","created":"2021-02-12","cited_by_count":128},{"id":"2010.02372","title":"Lower Bounds and Optimal Algorithms for Personalized Federated Learning","created":"2020-10-05","cited_by_count":123},{"id":"1907.09356","title":"Decentralized Deep Learning with Arbitrary Communication Compression","created":"2019-07-22","cited_by_count":111},{"id":"2206.08307","title":"Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning","created":"2022-06-16","cited_by_count":79},{"id":"2206.07638","title":"Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays","created":"2022-06-15","cited_by_count":63},{"id":"2012.14453","title":"Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity","created":"2020-12-28","cited_by_count":56},{"id":"2103.05032","title":"Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning","created":"2021-03-08","cited_by_count":52},{"id":"2106.02969","title":"FedNL: Making Newton-Type Methods Applicable to Federated Learning","created":"2021-06-05","cited_by_count":52},{"id":"1806.10188","title":"A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates","created":"2018-06-26","cited_by_count":47},{"id":"1904.10120","title":"Semi-Cyclic Stochastic Gradient Descent","created":"2019-04-23","cited_by_count":40},{"id":"2006.13838","title":"Advances in Asynchronous Parallel and Distributed Optimization","created":"2020-06-24","cited_by_count":40},{"id":"2102.03832","title":"Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks","created":"2021-02-07","cited_by_count":36},{"id":"2102.09743","title":"Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques","created":"2021-02-19","cited_by_count":34},{"id":"1605.09774","title":"Asynchrony begets Momentum, with an Application to Deep Learning","created":"2016-05-31","cited_by_count":30},{"id":"2002.07836","title":"Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning","created":"2020-02-18","cited_by_count":28},{"id":"1401.2596","title":"Differentially Private Distributed Optimization","created":"2014-01-12","cited_by_count":24},{"id":"1811.03982","title":"Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions","created":"2018-11-09","cited_by_count":20},{"id":"2002.05135","title":"On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning","created":"2020-02-12","cited_by_count":19},{"id":"2103.02351","title":"Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates","created":"2021-03-03","cited_by_count":17},{"id":"1912.07902","title":"Asynchronous Federated Learning with Differential Privacy for Edge Intelligence","created":"2019-12-17","cited_by_count":16},{"id":"2107.07190","title":"Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes","created":"2021-07-15","cited_by_count":13},{"id":"2111.11556","title":"FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning","created":"2021-11-22","cited_by_count":12},{"id":"2206.02468","title":"An Optimal Transport Approach to Personalized Federated Learning","created":"2022-06-06","cited_by_count":12},{"id":"2107.13173","title":"New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning","created":"2021-07-28","cited_by_count":11},{"id":"2203.13663","title":"FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning","created":"2022-03-24","cited_by_count":9},{"id":"2206.02078","title":"Straggler-Resilient Personalized Federated Learning","created":"2022-06-05","cited_by_count":9},{"id":"2008.02452","title":"Federated Transfer Learning with Dynamic Gradient Aggregation","created":"2020-08-06","cited_by_count":8},{"id":"2106.12261","title":"Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays","created":"2021-06-23","cited_by_count":7},{"id":"2107.10406","title":"Distributed Asynchronous Policy Iteration for Sequential Zero-Sum Games and Minimax Control","created":"2021-07-22","cited_by_count":6},{"id":"2209.05148","title":"Personalized Federated Learning with Communication Compression","created":"2022-09-12","cited_by_count":6},{"id":"2207.12779","title":"Reconciling Security and Communication Efficiency in Federated Learning","created":"2022-07-26","cited_by_count":4},{"id":"1802.08634","title":"Fully Asynchronous Push-Sum With Growing Intercommunication Intervals","created":"2018-02-23","cited_by_count":3},{"id":"2204.13619","title":"Personalized Federated Learning with Multiple Known Clusters","created":"2022-04-28","cited_by_count":3}],"next_cursor":null}