{"list":"other","total":107,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"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":"1907.02189","title":"On the Convergence of FedAvg on Non-IID Data","created":"2019-07-04","cited_by_count":1229},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"1705.02315","title":"ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases","created":"2017-05-05","cited_by_count":972},{"id":"1807.00459","title":"How To Backdoor Federated Learning","created":"2018-07-02","cited_by_count":765},{"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":"1803.01498","title":"Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates","created":"2018-03-05","cited_by_count":711},{"id":"2006.07242","title":"Ensemble Distillation for Robust Model Fusion in Federated Learning","created":"2020-06-12","cited_by_count":682},{"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":"1802.03471","title":"Certified Robustness to Adversarial Examples with Differential Privacy","created":"2018-02-09","cited_by_count":468},{"id":"1904.03758","title":"Meta-Learning with Differentiable Convex Optimization","created":"2019-04-07","cited_by_count":461},{"id":"2001.02610","title":"iDLG: Improved Deep Leakage from Gradients","created":"2020-01-08","cited_by_count":382},{"id":"1802.04633","title":"Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring","created":"2018-02-13","cited_by_count":357},{"id":"1811.12470","title":"Analyzing Federated Learning through an Adversarial Lens","created":"2018-11-29","cited_by_count":351},{"id":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"1802.07927","title":"The Hidden Vulnerability of Distributed Learning in Byzantium","created":"2018-02-22","cited_by_count":329},{"id":"2104.07586","title":"See through Gradients: Image Batch Recovery via GradInversion","created":"2021-04-15","cited_by_count":326},{"id":"2101.05930","title":"Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks","created":"2021-01-15","cited_by_count":324},{"id":"1912.13445","title":"Robust Aggregation for Federated Learning","created":"2019-12-31","cited_by_count":317},{"id":"2007.05084","title":"Attack of the Tails: Yes, You Really Can Backdoor Federated Learning","created":"2020-07-09","cited_by_count":309},{"id":"1902.06156","title":"A Little Is Enough: Circumventing Defenses For Distributed Learning","created":"2019-02-16","cited_by_count":288},{"id":"2007.08432","title":"Data Poisoning Attacks Against Federated Learning Systems","created":"2020-07-16","cited_by_count":275},{"id":"1911.07963","title":"Can You Really Backdoor Federated Learning?","created":"2019-11-18","cited_by_count":255},{"id":"2104.07914","title":"Federated Learning for Internet of Things: A Comprehensive Survey","created":"2021-04-16","cited_by_count":252},{"id":"1701.04082","title":"Embedding Watermarks into Deep Neural Networks","created":"2017-01-15","cited_by_count":248},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"1911.07135","title":"The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks","created":"2019-11-17","cited_by_count":246},{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184},{"id":"2003.02133","title":"Threats to Federated Learning: A Survey","created":"2020-03-04","cited_by_count":178},{"id":"1808.04866","title":"Mitigating Sybils in Federated Learning Poisoning","created":"2018-08-14","cited_by_count":161},{"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":"2108.10749","title":"Federated Learning for Open Banking","created":"2021-08-24","cited_by_count":141},{"id":"1306.4447","title":"Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers","created":"2013-06-19","cited_by_count":130},{"id":"2001.06202","title":"FedVision: An Online Visual Object Detection Platform Powered by Federated Learning","created":"2020-01-17","cited_by_count":124},{"id":"1905.08114","title":"Zero-Shot Knowledge Distillation in Deep Networks","created":"2019-05-20","cited_by_count":121},{"id":"1911.11607","title":"Deep Learning with Gaussian Differential Privacy","created":"2019-11-26","cited_by_count":114},{"id":"2110.14962","title":"Gradient Inversion with Generative Image Prior","created":"2021-10-28","cited_by_count":101},{"id":"2206.10341","title":"Neurotoxin: Durable Backdoors in Federated Learning","created":"2022-06-12","cited_by_count":98},{"id":"1802.10116","title":"Generalized Byzantine-tolerant SGD","created":"2018-02-27","cited_by_count":95},{"id":"2012.06043","title":"Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective","created":"2020-12-08","cited_by_count":94},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91},{"id":"2006.07228","title":"FedGAN: Federated Generative Adversarial Networks for Distributed Data","created":"2020-06-12","cited_by_count":90},{"id":"2007.03767","title":"Defending against Backdoors in Federated Learning with Robust Learning Rate","created":"2020-07-07","cited_by_count":82},{"id":"2201.00763","title":"DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection","created":"2022-01-03","cited_by_count":81},{"id":"1901.09888","title":"Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System","created":"2019-01-29","cited_by_count":79},{"id":"1903.09860","title":"Data Poisoning against Differentially-Private Learners: Attacks and Defenses","created":"2019-03-23","cited_by_count":78},{"id":"2203.15696","title":"Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage","created":"2022-03-29","cited_by_count":72},{"id":"2007.15789","title":"LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy","created":"2020-07-31","cited_by_count":64},{"id":"2004.10020","title":"Data Poisoning Attacks on Federated Machine Learning","created":"2020-04-19","cited_by_count":62},{"id":"2010.05867","title":"Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications","created":"2020-10-12","cited_by_count":58},{"id":"2006.11901","title":"Free-rider Attacks on Model Aggregation in Federated Learning","created":"2020-06-21","cited_by_count":57},{"id":"2111.10778","title":"Federated Social Recommendation with Graph Neural Network","created":"2021-11-21","cited_by_count":56},{"id":"2110.05754","title":"Deep Federated Learning for Autonomous Driving","created":"2021-10-12","cited_by_count":54},{"id":"2011.02167","title":"BaFFLe: Backdoor detection via Feedback-based Federated Learning","created":"2020-11-04","cited_by_count":53},{"id":"1812.07108","title":"Learning Private Neural Language Modeling with Attentive Aggregation","created":"2018-12-17","cited_by_count":49},{"id":"1911.12560","title":"Free-riders in Federated Learning: Attacks and Defenses","created":"2019-11-28","cited_by_count":49},{"id":"2112.14468","title":"Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning","created":"2021-12-29","cited_by_count":47},{"id":"1609.00408","title":"Defeating Image Obfuscation with Deep Learning","created":"2016-09-01","cited_by_count":43},{"id":"2107.12603","title":"Federated Learning Meets Natural Language Processing: A Survey","created":"2021-07-27","cited_by_count":43},{"id":"2107.01154","title":"Gradient-Leakage Resilient Federated Learning","created":"2021-07-02","cited_by_count":40},{"id":"2206.07284","title":"A Survey on Gradient Inversion: Attacks, Defenses and Future Directions","created":"2022-06-15","cited_by_count":40},{"id":"2011.01767","title":"Mitigating Backdoor Attacks in Federated Learning","created":"2020-10-28","cited_by_count":39},{"id":"2008.07298","title":"WAFFLE: Watermarking in Federated Learning","created":"2020-08-17","cited_by_count":37},{"id":"2110.10926","title":"PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion","created":"2021-10-21","cited_by_count":37},{"id":"2105.00529","title":"GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning","created":"2021-05-02","cited_by_count":36},{"id":"2109.13236","title":"FedIPR: Ownership Verification for Federated Deep Neural Network Models","created":"2021-09-27","cited_by_count":36},{"id":"1705.04862","title":"Efficient Parallel Methods for Deep Reinforcement Learning","created":"2017-05-13","cited_by_count":32},{"id":"2210.00584","title":"FLCert: Provably Secure Federated Learning against Poisoning Attacks","created":"2022-10-02","cited_by_count":30},{"id":"2108.04725","title":"PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage","created":"2021-08-10","cited_by_count":28},{"id":"2301.08170","title":"On the Vulnerability of Backdoor Defenses for Federated Learning","created":"2023-01-19","cited_by_count":27},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24},{"id":"2204.01499","title":"FedRecAttack: Model Poisoning Attack to Federated Recommendation","created":"2022-04-01","cited_by_count":20},{"id":"2002.09843","title":"An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning","created":"2020-02-23","cited_by_count":19},{"id":"2103.11879","title":"Real-time End-to-End Federated Learning: An Automotive Case Study","created":"2021-03-22","cited_by_count":15},{"id":"2303.07666","title":"Relational Multi-Task Learning: Modeling Relations between Data and Tasks","created":"2023-03-14","cited_by_count":15},{"id":"2004.12108","title":"Privacy Preserving Distributed Machine Learning with Federated Learning","created":"2020-04-25","cited_by_count":13},{"id":"2204.13594","title":"Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios","created":"2022-04-26","cited_by_count":13},{"id":"1806.06151","title":"Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining","created":"2018-06-15","cited_by_count":12},{"id":"2112.01405","title":"FedRAD: Federated Robust Adaptive Distillation","created":"2021-12-02","cited_by_count":11},{"id":"2106.09993","title":"Accumulative Poisoning Attacks on Real-time Data","created":"2021-06-18","cited_by_count":10},{"id":"2203.11633","title":"Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis","created":"2022-03-22","cited_by_count":9},{"id":"2204.14017","title":"Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling","created":"2022-04-29","cited_by_count":9},{"id":"2303.03320","title":"Learning to Backdoor Federated Learning","created":"2023-03-06","cited_by_count":8}],"next_cursor":"100"}