{"list":"other","total":51,"rows":[{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"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":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"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":"2002.06440","title":"Federated Learning with Matched Averaging","created":"2020-02-15","cited_by_count":564},{"id":"2203.14371","title":"MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering","created":"2022-03-27","cited_by_count":543},{"id":"1902.04885","title":"Federated Machine Learning: Concept and Applications","created":"2019-02-13","cited_by_count":412},{"id":"2007.13518","title":"FedML: A Research Library and Benchmark for Federated Machine Learning","created":"2020-07-27","cited_by_count":300},{"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":"1907.09173","title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","created":"2019-07-22","cited_by_count":182},{"id":"2003.02133","title":"Threats to Federated Learning: A Survey","created":"2020-03-04","cited_by_count":178},{"id":"1901.08755","title":"SecureBoost: A Lossless Federated Learning Framework","created":"2019-01-25","cited_by_count":176},{"id":"1910.00962","title":"Privacy-preserving Federated Brain Tumour Segmentation","created":"2019-10-02","cited_by_count":169},{"id":"1910.10252","title":"Federated Evaluation of On-device Personalization","created":"2019-10-22","cited_by_count":147},{"id":"2002.04758","title":"Salvaging Federated Learning by Local Adaptation","created":"2020-02-12","cited_by_count":142},{"id":"2001.05647","title":"Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results","created":"2020-01-16","cited_by_count":122},{"id":"1811.04017","title":"A generic framework for privacy preserving deep learning","created":"2018-11-09","cited_by_count":103},{"id":"1906.05108","title":"Secure Federated Matrix Factorization","created":"2019-06-12","cited_by_count":102},{"id":"1903.07424","title":"Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation","created":"2019-03-18","cited_by_count":100},{"id":"2002.09668","title":"Communication-Efficient Edge AI: Algorithms and Systems","created":"2020-02-22","cited_by_count":96},{"id":"1611.03814","title":"Towards the Science of Security and Privacy in Machine Learning","created":"2016-11-11","cited_by_count":89},{"id":"2001.08103","title":"Secure and Robust Machine Learning for Healthcare: A Survey","created":"2020-01-21","cited_by_count":80},{"id":"1804.11238","title":"Privacy Preserving Machine Learning: Threats and Solutions","created":"2018-03-27","cited_by_count":79},{"id":"1903.09296","title":"Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records","created":"2019-03-22","cited_by_count":72},{"id":"2009.01871","title":"Federated Learning for Breast Density Classification: A Real-World Implementation","created":"2020-09-03","cited_by_count":62},{"id":"2012.07450","title":"FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring","created":"2020-12-14","cited_by_count":61},{"id":"2101.07511","title":"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge","created":"2021-01-19","cited_by_count":56},{"id":"1911.09824","title":"Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator","created":"2019-11-22","cited_by_count":52},{"id":"1905.10053","title":"Federated Forest","created":"2019-05-24","cited_by_count":49},{"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":"2009.05868","title":"From Federated Learning to Federated Neural Architecture Search: A Survey","created":"2020-09-12","cited_by_count":42},{"id":"2004.12321","title":"Federated Transfer Learning for EEG Signal Classification","created":"2020-04-26","cited_by_count":37},{"id":"1910.04956","title":"Central Server Free Federated Learning over Single-sided Trust Social Networks","created":"2019-10-11","cited_by_count":33},{"id":"2103.03918","title":"FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data","created":"2021-03-05","cited_by_count":31},{"id":"1812.03288","title":"No Peek: A Survey of private distributed deep learning","created":"2018-12-08","cited_by_count":30},{"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":"2011.11750","title":"Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan","created":"2020-11-23","cited_by_count":23},{"id":"2007.00914","title":"Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy","created":"2020-07-02","cited_by_count":22},{"id":"1911.04209","title":"Privacy-Preserving Gradient Boosting Decision Trees","created":"2019-11-11","cited_by_count":20},{"id":"2101.02373","title":"Architectural Patterns for the Design of Federated Learning Systems","created":"2021-01-07","cited_by_count":17},{"id":"2010.02417","title":"Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing","created":"2020-10-06","cited_by_count":13},{"id":"2102.12245","title":"Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach","created":"2021-02-24","cited_by_count":12},{"id":"1811.11400","title":"FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record","created":"2018-11-28","cited_by_count":11},{"id":"2006.10517","title":"Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention","created":"2020-06-15","cited_by_count":11},{"id":"2007.14513","title":"Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge","created":"2020-07-28","cited_by_count":10},{"id":"1910.02175","title":"Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images","created":"2019-10-05","cited_by_count":5},{"id":"2001.09751","title":"Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records","created":"2019-12-25","cited_by_count":4}],"next_cursor":null}