{"list":"other","total":51,"rows":[{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"1706.02677","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","created":"2017-06-08","cited_by_count":1492},{"id":"1806.00582","title":"Federated Learning with Non-IID Data","created":"2018-06-02","cited_by_count":1108},{"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":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"2105.00243","title":"FedProto: Federated Prototype Learning across Heterogeneous Clients","created":"2021-05-01","cited_by_count":352},{"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":"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":"1902.10275","title":"Towards Efficient Data Valuation Based on the Shapley Value","created":"2019-02-27","cited_by_count":281},{"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":"1808.04866","title":"Mitigating Sybils in Federated Learning Poisoning","created":"2018-08-14","cited_by_count":161},{"id":"1903.03936","title":"Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation","created":"2019-03-10","cited_by_count":141},{"id":"2108.10749","title":"Federated Learning for Open Banking","created":"2021-08-24","cited_by_count":141},{"id":"2006.09365","title":"Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing","created":"2020-06-16","cited_by_count":128},{"id":"2009.06192","title":"A Principled Approach to Data Valuation for Federated Learning","created":"2020-09-14","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":"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":"2008.12161","title":"Collaborative Fairness in Federated Learning","created":"2020-08-27","cited_by_count":95},{"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":"1909.08525","title":"Measure Contribution of Participants in Federated Learning","created":"2019-09-17","cited_by_count":79},{"id":"2109.02053","title":"GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning","created":"2021-09-05","cited_by_count":74},{"id":"1909.05125","title":"Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging","created":"2019-09-11","cited_by_count":70},{"id":"2203.08669","title":"MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients","created":"2022-03-16","cited_by_count":68},{"id":"2111.01872","title":"Towards Fairness-Aware Federated Learning","created":"2021-11-02","cited_by_count":58},{"id":"2210.10936","title":"FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information","created":"2022-10-20","cited_by_count":58},{"id":"2303.16520","title":"Fair Federated Medical Image Segmentation via Client Contribution Estimation","created":"2023-03-29","cited_by_count":52},{"id":"1911.12560","title":"Free-riders in Federated Learning: Attacks and Defenses","created":"2019-11-28","cited_by_count":49},{"id":"2002.09699","title":"FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC","created":"2020-02-22","cited_by_count":48},{"id":"2112.14468","title":"Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning","created":"2021-12-29","cited_by_count":47},{"id":"2011.10464","title":"A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning","created":"2020-11-20","cited_by_count":41},{"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":"2001.11359","title":"FOCUS: Dealing with Label Quality Disparity in Federated Learning","created":"2020-01-29","cited_by_count":36},{"id":"2212.06325","title":"AFLGuard: Byzantine-robust Asynchronous Federated Learning","created":"2022-12-13","cited_by_count":29},{"id":"2301.09508","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","created":"2023-01-23","cited_by_count":23},{"id":"2109.09046","title":"Improving Fairness for Data Valuation in Horizontal Federated Learning","created":"2021-09-19","cited_by_count":22},{"id":"2201.02658","title":"Fair and efficient contribution valuation for vertical federated learning","created":"2022-01-07","cited_by_count":18},{"id":"2104.07515","title":"FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems","created":"2021-04-15","cited_by_count":16},{"id":"2209.04856","title":"Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)","created":"2022-09-11","cited_by_count":13},{"id":"2305.08070","title":"A Survey of Federated Evaluation in Federated Learning","created":"2023-05-14","cited_by_count":7},{"id":"2201.13086","title":"Securing Federated Sensitive Topic Classification against Poisoning Attacks","created":"2022-01-31","cited_by_count":6}],"next_cursor":null}