{"list":"other","total":63,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"2003.00295","title":"Adaptive Federated Optimization","created":"2020-02-29","cited_by_count":1046},{"id":"1812.01097","title":"LEAF: A Benchmark for Federated Settings","created":"2018-12-03","cited_by_count":792},{"id":"2103.16257","title":"Model-Contrastive Federated Learning","created":"2021-03-30","cited_by_count":766},{"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":"2012.04221","title":"Ditto: Fair and Robust Federated Learning Through Personalization","created":"2020-12-08","cited_by_count":672},{"id":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"1902.06705","title":"On Evaluating Adversarial Robustness","created":"2019-02-18","cited_by_count":447},{"id":"1712.03141","title":"Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning","created":"2017-12-08","cited_by_count":414},{"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":"1802.07927","title":"The Hidden Vulnerability of Distributed Learning in Byzantium","created":"2018-02-22","cited_by_count":329},{"id":"1912.13445","title":"Robust Aggregation for Federated Learning","created":"2019-12-31","cited_by_count":317},{"id":"1902.06156","title":"A Little Is Enough: Circumventing Defenses For Distributed Learning","created":"2019-02-16","cited_by_count":288},{"id":"1911.07963","title":"Can You Really Backdoor Federated Learning?","created":"2019-11-18","cited_by_count":255},{"id":"2002.05516","title":"Federated Learning of a Mixture of Global and Local Models","created":"2020-02-10","cited_by_count":210},{"id":"1708.08689","title":"Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization","created":"2017-08-29","cited_by_count":197},{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184},{"id":"2012.10544","title":"Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses","created":"2020-12-18","cited_by_count":180},{"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":"2012.10333","title":"Learning from History for Byzantine Robust Optimization","created":"2020-12-18","cited_by_count":137},{"id":"2006.09365","title":"Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing","created":"2020-06-16","cited_by_count":128},{"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":"2210.04620","title":"FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings","created":"2022-10-10","cited_by_count":125},{"id":"2010.02372","title":"Lower Bounds and Optimal Algorithms for Personalized Federated Learning","created":"2020-10-05","cited_by_count":123},{"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":"1802.10116","title":"Generalized Byzantine-tolerant SGD","created":"2018-02-27","cited_by_count":95},{"id":"1912.11279","title":"Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer","created":"2019-12-24","cited_by_count":93},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91},{"id":"2002.00211","title":"Learning to Detect Malicious Clients for Robust Federated Learning","created":"2020-02-01","cited_by_count":88},{"id":"2102.08503","title":"Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications","created":"2021-02-16","cited_by_count":85},{"id":"2201.08135","title":"Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges","created":"2022-01-20","cited_by_count":84},{"id":"2007.03767","title":"Defending against Backdoors in Federated Learning with Robust Learning Rate","created":"2020-07-07","cited_by_count":82},{"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":"2102.01854","title":"Provably Secure Federated Learning against Malicious Clients","created":"2021-02-03","cited_by_count":63},{"id":"2210.10936","title":"FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information","created":"2022-10-20","cited_by_count":58},{"id":"2012.14368","title":"Byzantine-Resilient Non-Convex Stochastic Gradient Descent","created":"2020-12-28","cited_by_count":57},{"id":"2011.02167","title":"BaFFLe: Backdoor detection via Feedback-based Federated Learning","created":"2020-11-04","cited_by_count":53},{"id":"1910.09933","title":"Abnormal Client Behavior Detection in Federated Learning","created":"2019-10-22","cited_by_count":40},{"id":"2011.01767","title":"Mitigating Backdoor Attacks in Federated Learning","created":"2020-10-28","cited_by_count":39},{"id":"2210.12873","title":"FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning","created":"2022-10-23","cited_by_count":37},{"id":"2110.12257","title":"Game of Gradients: Mitigating Irrelevant Clients in Federated Learning","created":"2021-10-23","cited_by_count":35},{"id":"2108.02117","title":"FedJAX: Federated learning simulation with JAX","created":"2021-08-04","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":"2112.12727","title":"EIFFeL: Ensuring Integrity for Federated Learning","created":"2021-12-23","cited_by_count":28},{"id":"2102.00655","title":"Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning","created":"2021-02-01","cited_by_count":21},{"id":"1809.03474","title":"Universal Multi-Party Poisoning Attacks","created":"2018-09-10","cited_by_count":17},{"id":"2207.01982","title":"Defending against the Label-flipping Attack in Federated Learning","created":"2022-07-05","cited_by_count":16},{"id":"2101.10904","title":"Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation","created":"2021-01-24","cited_by_count":13},{"id":"2012.04436","title":"Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things","created":"2020-12-08","cited_by_count":9},{"id":"2107.01477","title":"Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates","created":"2021-07-03","cited_by_count":8},{"id":"2110.04350","title":"FRL: Federated Rank Learning","created":"2021-10-08","cited_by_count":8},{"id":"2209.14093","title":"Securing Federated Learning against Overwhelming Collusive Attackers","created":"2022-09-28","cited_by_count":5},{"id":"2211.05554","title":"Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization","created":"2022-11-10","cited_by_count":3},{"id":"2106.06891","title":"Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning","created":"2021-06-13","cited_by_count":1}],"next_cursor":null}