{"list":"other","total":65,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1910.01108","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","created":"2019-10-02","cited_by_count":4307},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1602.07360","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","created":"2016-02-24","cited_by_count":1060},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"1810.04650","title":"Multi-Task Learning as Multi-Objective Optimization","created":"2018-10-10","cited_by_count":821},{"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":"2003.08119","title":"The Future of Digital Health with Federated Learning","created":"2020-03-18","cited_by_count":633},{"id":"1811.03604","title":"Federated Learning for Mobile Keyboard Prediction","created":"2018-11-08","cited_by_count":572},{"id":"1610.02527","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","created":"2016-10-08","cited_by_count":564},{"id":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"2001.02610","title":"iDLG: Improved Deep Leakage from Gradients","created":"2020-01-08","cited_by_count":382},{"id":"1910.00033","title":"Hidden Trigger Backdoor Attacks","created":"2019-09-30","cited_by_count":371},{"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":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346},{"id":"1802.07927","title":"The Hidden Vulnerability of Distributed Learning in Byzantium","created":"2018-02-22","cited_by_count":329},{"id":"2007.05084","title":"Attack of the Tails: Yes, You Really Can Backdoor Federated Learning","created":"2020-07-09","cited_by_count":309},{"id":"1912.02771","title":"Label-Consistent Backdoor Attacks","created":"2019-12-05","cited_by_count":296},{"id":"1911.07963","title":"Can You Really Backdoor Federated Learning?","created":"2019-11-18","cited_by_count":255},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"2006.01043","title":"BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements","created":"2020-06-01","cited_by_count":211},{"id":"1812.02903","title":"Applied Federated Learning: Improving Google Keyboard Query Suggestions","created":"2018-12-07","cited_by_count":204},{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184},{"id":"2005.03823","title":"Blind Backdoors in Deep Learning Models","created":"2020-05-08","cited_by_count":182},{"id":"1802.07876","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","created":"2018-02-22","cited_by_count":155},{"id":"1810.04304","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","created":"2018-10-10","cited_by_count":149},{"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":"1903.03936","title":"Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation","created":"2019-03-10","cited_by_count":141},{"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":"2001.06202","title":"FedVision: An Online Visual Object Detection Platform Powered by Federated Learning","created":"2020-01-17","cited_by_count":124},{"id":"2007.10760","title":"Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review","created":"2020-07-21","cited_by_count":123},{"id":"1904.01067","title":"Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning","created":"2019-04-01","cited_by_count":122},{"id":"1906.04329","title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","created":"2019-06-11","cited_by_count":118},{"id":"1806.03287","title":"Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware","created":"2018-06-08","cited_by_count":102},{"id":"2104.14380","title":"PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments","created":"2021-04-29","cited_by_count":93},{"id":"1803.06975","title":"Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks","created":"2018-03-19","cited_by_count":81},{"id":"2201.00763","title":"DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection","created":"2022-01-03","cited_by_count":81},{"id":"1912.12716","title":"Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks","created":"2019-12-29","cited_by_count":79},{"id":"2009.03561","title":"Local and Central Differential Privacy for Robustness and Privacy in Federated Learning","created":"2020-09-08","cited_by_count":78},{"id":"2106.09898","title":"Bad Characters: Imperceptible NLP Attacks","created":"2021-06-18","cited_by_count":71},{"id":"1708.06145","title":"Knock Knock, Who's There? Membership Inference on Aggregate Location Data","created":"2017-08-21","cited_by_count":70},{"id":"1909.05125","title":"Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging","created":"2019-09-11","cited_by_count":70},{"id":"2004.10020","title":"Data Poisoning Attacks on Federated Machine Learning","created":"2020-04-19","cited_by_count":62},{"id":"2009.01871","title":"Federated Learning for Breast Density Classification: A Real-World Implementation","created":"2020-09-03","cited_by_count":62},{"id":"1704.02147","title":"Hierarchical Clustering: Objective Functions and Algorithms","created":"2017-04-07","cited_by_count":60},{"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":"1903.10635","title":"Federated Learning Of Out-Of-Vocabulary Words","created":"2019-03-26","cited_by_count":51},{"id":"1810.08553","title":"Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data","created":"2018-10-19","cited_by_count":46},{"id":"1710.06963","title":"Learning Differentially Private Recurrent Language Models","created":"2017-10-18","cited_by_count":36},{"id":"1910.06044","title":"Eavesdrop the Composition Proportion of Training Labels in Federated Learning","created":"2019-10-14","cited_by_count":27},{"id":"1910.13111","title":"Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection","created":"2019-10-29","cited_by_count":26},{"id":"2301.09508","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","created":"2023-01-23","cited_by_count":23},{"id":"2112.12496","title":"FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition","created":"2021-12-23","cited_by_count":16},{"id":"1802.07295","title":"Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning","created":"2018-02-20","cited_by_count":14},{"id":"2105.02295","title":"Byzantine-Robust and Privacy-Preserving Framework for FedML","created":"2021-05-05","cited_by_count":14},{"id":"2101.08204","title":"secureTF: A Secure TensorFlow Framework","created":"2021-01-20","cited_by_count":12},{"id":"2111.13230","title":"FedDropoutAvg: Generalizable federated learning for histopathology image classification","created":"2021-11-25","cited_by_count":7},{"id":"1904.04782","title":"Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using Heterogeneous Trusted Execution Environment","created":"2019-04-09","cited_by_count":3}],"next_cursor":null}