{"list":"other","total":59,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"2304.01373","title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling","created":"2023-04-03","cited_by_count":1786},{"id":"1910.02054","title":"ZeRO: Memory Optimizations Toward Training Trillion Parameter Models","created":"2019-10-04","cited_by_count":1301},{"id":"1909.06146","title":"PubMedQA: A Dataset for Biomedical Research Question Answering","created":"2019-09-13","cited_by_count":1288},{"id":"2003.00295","title":"Adaptive Federated Optimization","created":"2020-02-29","cited_by_count":1046},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2007.07481","title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","created":"2020-07-15","cited_by_count":906},{"id":"1812.01097","title":"LEAF: A Benchmark for Federated Settings","created":"2018-12-03","cited_by_count":792},{"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":"1811.03604","title":"Federated Learning for Mobile Keyboard Prediction","created":"2018-11-08","cited_by_count":572},{"id":"2210.10341","title":"BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining","created":"2022-10-19","cited_by_count":538},{"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":"2007.14390","title":"Flower: A Friendly Federated Learning Research Framework","created":"2020-07-28","cited_by_count":455},{"id":"1811.12470","title":"Analyzing Federated Learning through an Adversarial Lens","created":"2018-11-29","cited_by_count":351},{"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":"2007.13518","title":"FedML: A Research Library and Benchmark for Federated Machine Learning","created":"2020-07-27","cited_by_count":300},{"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":"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":"2012.10333","title":"Learning from History for Byzantine Robust Optimization","created":"2020-12-18","cited_by_count":137},{"id":"2105.11367","title":"FedScale: Benchmarking Model and System Performance of Federated Learning at Scale","created":"2021-05-24","cited_by_count":136},{"id":"2006.09365","title":"Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing","created":"2020-06-16","cited_by_count":128},{"id":"1906.04329","title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","created":"2019-06-11","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":"2010.10152","title":"Feature Inference Attack on Model Predictions in Vertical Federated Learning","created":"2020-10-20","cited_by_count":106},{"id":"2206.10341","title":"Neurotoxin: Durable Backdoors in Federated Learning","created":"2022-06-12","cited_by_count":98},{"id":"1810.05512","title":"Federated Learning for Keyword Spotting","created":"2018-10-09","cited_by_count":95},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91},{"id":"2101.05428","title":"Federated Learning: Opportunities and Challenges","created":"2021-01-14","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":"2210.13291","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","created":"2022-10-24","cited_by_count":80},{"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":"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":"2007.10987","title":"IBM Federated Learning: an Enterprise Framework White Paper V0.1","created":"2020-07-22","cited_by_count":57},{"id":"1903.10635","title":"Federated Learning Of Out-Of-Vocabulary Words","created":"2019-03-26","cited_by_count":51},{"id":"1911.12560","title":"Free-riders in Federated Learning: Attacks and Defenses","created":"2019-11-28","cited_by_count":49},{"id":"1912.11464","title":"Attack-Resistant Federated Learning with Residual-based Reweighting","created":"2019-12-24","cited_by_count":40},{"id":"2110.13864","title":"FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective","created":"2021-10-26","cited_by_count":36},{"id":"2109.05659","title":"Source Inference Attacks in Federated Learning","created":"2021-09-13","cited_by_count":34},{"id":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","cited_by_count":31},{"id":"2004.08546","title":"Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search","created":"2020-04-18","cited_by_count":30},{"id":"2109.05872","title":"Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering","created":"2021-09-13","cited_by_count":30},{"id":"2203.13789","title":"FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations","created":"2022-03-25","cited_by_count":27},{"id":"2301.09508","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","created":"2023-01-23","cited_by_count":23},{"id":"2203.09553","title":"Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation","created":"2022-03-17","cited_by_count":18},{"id":"2111.00556","title":"Revealing and Protecting Labels in Distributed Training","created":"2021-10-31","cited_by_count":17},{"id":"1909.04532","title":"Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach","created":"2019-09-10","cited_by_count":13},{"id":"2302.01326","title":"Federated Analytics: A survey","created":"2023-02-02","cited_by_count":9},{"id":"2206.05359","title":"Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning","created":"2022-06-10","cited_by_count":7}],"next_cursor":null}