{"list":"other","total":64,"rows":[{"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":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"1807.00459","title":"How To Backdoor Federated Learning","created":"2018-07-02","cited_by_count":765},{"id":"1803.01498","title":"Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates","created":"2018-03-05","cited_by_count":711},{"id":"1802.04434","title":"signSGD: Compressed Optimisation for Non-Convex Problems","created":"2018-02-13","cited_by_count":571},{"id":"1805.04049","title":"Exploiting Unintended Feature Leakage in Collaborative Learning","created":"2018-05-10","cited_by_count":520},{"id":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"1809.07599","title":"Sparsified SGD with Memory","created":"2018-09-20","cited_by_count":376},{"id":"1811.12470","title":"Analyzing Federated Learning through an Adversarial Lens","created":"2018-11-29","cited_by_count":351},{"id":"2012.13995","title":"FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping","created":"2020-12-27","cited_by_count":346},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315},{"id":"1902.06156","title":"A Little Is Enough: Circumventing Defenses For Distributed Learning","created":"2019-02-16","cited_by_count":288},{"id":"1705.07878","title":"TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning","created":"2017-05-22","cited_by_count":256},{"id":"1911.07963","title":"Can You Really Backdoor Federated Learning?","created":"2019-11-18","cited_by_count":255},{"id":"1704.05021","title":"Sparse Communication for Distributed Gradient Descent","created":"2017-04-17","cited_by_count":249},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"1809.10505","title":"The Convergence of Sparsified Gradient Methods","created":"2018-09-27","cited_by_count":229},{"id":"1801.05507","title":"Gazelle: A Low Latency Framework for Secure Neural Network Inference","created":"2018-01-16","cited_by_count":193},{"id":"2007.07682","title":"FetchSGD: Communication-Efficient Federated Learning with Sketching","created":"2020-07-15","cited_by_count":179},{"id":"1812.07210","title":"Expanding the Reach of Federated Learning by Reducing Client Resource Requirements","created":"2018-12-18","cited_by_count":163},{"id":"1811.03761","title":"RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets","created":"2018-11-09","cited_by_count":149},{"id":"2002.12410","title":"On Biased Compression for Distributed Learning","created":"2020-02-27","cited_by_count":140},{"id":"1611.00429","title":"Distributed Mean Estimation with Limited Communication","created":"2016-11-02","cited_by_count":136},{"id":"2106.05203","title":"EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback","created":"2021-06-09","cited_by_count":133},{"id":"2112.02918","title":"When the Curious Abandon Honesty: Federated Learning Is Not Private","created":"2021-12-06","cited_by_count":127},{"id":"2207.09209","title":"FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients","created":"2022-07-19","cited_by_count":125},{"id":"2108.10241","title":"Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning","created":"2021-08-23","cited_by_count":118},{"id":"2110.13057","title":"Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models","created":"2021-10-25","cited_by_count":108},{"id":"2206.10341","title":"Neurotoxin: Durable Backdoors in Federated Learning","created":"2022-06-12","cited_by_count":98},{"id":"1804.07933","title":"Is feature selection secure against training data poisoning?","created":"2018-04-21","cited_by_count":93},{"id":"1903.04488","title":"Communication-efficient distributed SGD with Sketching","created":"2019-03-12","cited_by_count":81},{"id":"2002.04156","title":"Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning","created":"2020-02-11","cited_by_count":78},{"id":"2102.02888","title":"1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed","created":"2021-02-04","cited_by_count":75},{"id":"2202.00580","title":"Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification","created":"2022-02-01","cited_by_count":73},{"id":"1708.06145","title":"Knock Knock, Who's There? Membership Inference on Aggregate Location Data","created":"2017-08-21","cited_by_count":70},{"id":"2111.07380","title":"Eluding Secure Aggregation in Federated Learning via Model Inconsistency","created":"2021-11-14","cited_by_count":67},{"id":"2009.11248","title":"FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning","created":"2020-09-23","cited_by_count":61},{"id":"2108.08842","title":"EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning","created":"2021-08-19","cited_by_count":55},{"id":"2011.02167","title":"BaFFLe: Backdoor detection via Feedback-based Federated Learning","created":"2020-11-04","cited_by_count":53},{"id":"2105.08339","title":"DRIVE: One-bit Distributed Mean Estimation","created":"2021-05-18","cited_by_count":52},{"id":"1902.07342","title":"XONN: XNOR-based Oblivious Deep Neural Network Inference","created":"2019-02-19","cited_by_count":51},{"id":"2002.08958","title":"Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor","created":"2020-02-20","cited_by_count":45},{"id":"2106.06089","title":"Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix","created":"2021-06-10","cited_by_count":43},{"id":"2106.03328","title":"Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning","created":"2021-06-07","cited_by_count":39},{"id":"2203.03761","title":"The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning","created":"2022-03-07","cited_by_count":39},{"id":"1703.06255","title":"Prio: Private, Robust, and Scalable Computation of Aggregate Statistics","created":"2017-03-18","cited_by_count":36},{"id":"2002.10940","title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees","created":"2020-02-25","cited_by_count":35},{"id":"2005.09042","title":"BLAZE: Blazing Fast Privacy-Preserving Machine Learning","created":"2020-05-18","cited_by_count":32},{"id":"1910.06044","title":"Eavesdrop the Composition Proportion of Training Labels in Federated Learning","created":"2019-10-14","cited_by_count":27},{"id":"1912.02631","title":"Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning","created":"2019-12-05","cited_by_count":26},{"id":"2009.00349","title":"POSEIDON: Privacy-Preserving Federated Neural Network Learning","created":"2020-09-01","cited_by_count":24},{"id":"2005.10296","title":"SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning","created":"2020-05-20","cited_by_count":20},{"id":"1912.02592","title":"ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction","created":"2019-12-05","cited_by_count":18},{"id":"2205.06117","title":"Secure Aggregation for Federated Learning in Flower","created":"2022-05-12","cited_by_count":15},{"id":"2010.02331","title":"How to send a real number using a single bit (and some shared randomness)","created":"2020-10-05","cited_by_count":12},{"id":"2106.02850","title":"Tetrad: Actively Secure 4PC for Secure Training and Inference","created":"2021-06-05","cited_by_count":8},{"id":"2205.13341","title":"QUIC-FL: Quick Unbiased Compression for Federated Learning","created":"2022-05-26","cited_by_count":7},{"id":"2007.14861","title":"Efficient Sparse Secure Aggregation for Federated Learning","created":"2020-07-29","cited_by_count":6},{"id":"2201.07730","title":"SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation","created":"2022-01-19","cited_by_count":4},{"id":"2112.13338","title":"MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning","created":"2021-12-26","cited_by_count":3},{"id":"2206.12224","title":"MPClan: Protocol Suite for Privacy-Conscious Computations","created":"2022-06-24","cited_by_count":1}],"next_cursor":null}