{"list":"other","total":67,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1908.07873","title":"Federated Learning: Challenges, Methods, and Future Directions","created":"2019-08-21","cited_by_count":1538},{"id":"1606.06650","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","created":"2016-06-21","cited_by_count":1476},{"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":"1909.06335","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","created":"2019-09-13","cited_by_count":851},{"id":"1902.01046","title":"Towards Federated Learning at Scale: System Design","created":"2019-02-04","cited_by_count":832},{"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":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"1712.01887","title":"Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training","created":"2017-12-05","cited_by_count":478},{"id":"2007.14390","title":"Flower: A Friendly Federated Learning Research Framework","created":"2020-07-28","cited_by_count":455},{"id":"1903.02891","title":"Robust and Communication-Efficient Federated Learning from Non-IID Data","created":"2019-03-07","cited_by_count":419},{"id":"2001.02610","title":"iDLG: Improved Deep Leakage from Gradients","created":"2020-01-08","cited_by_count":382},{"id":"1804.08333","title":"Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge","created":"2018-04-23","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":"2104.07586","title":"See through Gradients: Image Batch Recovery via GradInversion","created":"2021-04-15","cited_by_count":326},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315},{"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":"1903.03934","title":"Asynchronous Federated Optimization","created":"2019-03-10","cited_by_count":267},{"id":"2010.01243","title":"Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies","created":"2020-10-03","cited_by_count":198},{"id":"2106.06639","title":"Federated Learning with Buffered Asynchronous Aggregation","created":"2021-06-11","cited_by_count":190},{"id":"2007.01154","title":"Federated Learning with Compression: Unified Analysis and Sharp Guarantees","created":"2020-07-02","cited_by_count":160},{"id":"2309.00363","title":"FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning","created":"2023-09-01","cited_by_count":149},{"id":"2105.11367","title":"FedScale: Benchmarking Model and System Performance of Federated Learning at Scale","created":"2021-05-24","cited_by_count":136},{"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":"1906.04329","title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","created":"2019-06-11","cited_by_count":118},{"id":"2104.11375","title":"Decentralized Federated Averaging","created":"2021-04-23","cited_by_count":114},{"id":"2211.12814","title":"Vertical Federated Learning: Concepts, Advances and Challenges","created":"2022-11-23","cited_by_count":108},{"id":"1909.02362","title":"Hierarchical Federated Learning Across Heterogeneous Cellular Networks","created":"2019-09-05","cited_by_count":98},{"id":"1810.05512","title":"Federated Learning for Keyword Spotting","created":"2018-10-09","cited_by_count":95},{"id":"2109.04269","title":"Asynchronous Federated Learning on Heterogeneous Devices: A Survey","created":"2021-09-09","cited_by_count":93},{"id":"1906.06629","title":"Robust Federated Learning in a Heterogeneous Environment","created":"2019-06-16","cited_by_count":90},{"id":"2204.10836","title":"Federated Learning Enables Big Data for Rare Cancer Boundary Detection","created":"2022-04-22","cited_by_count":84},{"id":"2306.15546","title":"When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions","created":"2023-06-27","cited_by_count":84},{"id":"2210.13291","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","created":"2022-10-24","cited_by_count":80},{"id":"1909.08525","title":"Measure Contribution of Participants in Federated Learning","created":"2019-09-17","cited_by_count":79},{"id":"2207.14130","title":"FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning","created":"2022-07-28","cited_by_count":64},{"id":"2105.06413","title":"OpenFL: An open-source framework for Federated Learning","created":"2021-05-13","cited_by_count":58},{"id":"2107.11621","title":"FedLab: A Flexible Federated Learning Framework","created":"2021-07-24","cited_by_count":55},{"id":"2204.05011","title":"FederatedScope: A Flexible Federated Learning Platform for Heterogeneity","created":"2022-04-11","cited_by_count":53},{"id":"2202.06924","title":"Do Gradient Inversion Attacks Make Federated Learning Unsafe?","created":"2022-02-14","cited_by_count":44},{"id":"1905.04519","title":"Interpret Federated Learning with Shapley Values","created":"2019-05-11","cited_by_count":33},{"id":"2111.02925","title":"SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors","created":"2021-11-04","cited_by_count":33},{"id":"2005.04215","title":"funcX: A Federated Function Serving Fabric for Science","created":"2020-05-07","cited_by_count":32},{"id":"1802.06058","title":"Variance-based Gradient Compression for Efficient Distributed Deep Learning","created":"2018-02-16","cited_by_count":22},{"id":"2202.03672","title":"APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning","created":"2022-02-08","cited_by_count":20},{"id":"2110.15318","title":"Communication-Efficient ADMM-based Federated Learning","created":"2021-10-28","cited_by_count":14},{"id":"2309.14675","title":"FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler","created":"2023-09-26","cited_by_count":13},{"id":"2306.12079","title":"FLGo: A Fully Customizable Federated Learning Platform","created":"2023-06-21","cited_by_count":12},{"id":"2406.19256","title":"AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI","created":"2024-06-27","cited_by_count":9},{"id":"2407.01764","title":"Object Proxy Patterns for Accelerating Distributed Applications","created":"2024-07-01","cited_by_count":5},{"id":"2305.09593","title":"Accelerating Communications in Federated Applications with Transparent Object Proxies","created":"2023-05-16","cited_by_count":4},{"id":"2308.08786","title":"APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service","created":"2023-08-17","cited_by_count":4},{"id":"2312.13461","title":"FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications","created":"2023-12-20","cited_by_count":4},{"id":"2309.13194","title":"Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients","created":"2023-09-22","cited_by_count":3},{"id":"2402.12271","title":"Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2","created":"2024-02-19","cited_by_count":3},{"id":"2404.01517","title":"Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers","created":"2024-04-01","cited_by_count":1}],"next_cursor":null}