A Survey on federated learning*
暂无分享,去创建一个
Kuo-Yi Lin | Li Li | Yuxi Fan | Kuo-Yi Lin | Li Li | Yuxi Fan
[1] Anit Kumar Sahu,et al. Federated Optimization in Heterogeneous Networks , 2018, MLSys.
[2] Eryk Dutkiewicz,et al. Energy Demand Prediction with Federated Learning for Electric Vehicle Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).
[3] Paul M. Thompson,et al. Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[4] Satoshi Nakamoto. Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .
[5] Kan Yang,et al. VerifyNet: Secure and Verifiable Federated Learning , 2020, IEEE Transactions on Information Forensics and Security.
[6] Choong Seon Hong,et al. FLchain: Federated Learning via MEC-enabled Blockchain Network , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).
[7] Xiaoyan Sun,et al. Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[8] Takayuki Nishio,et al. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[9] Wei Shi,et al. Federated learning of predictive models from federated Electronic Health Records , 2018, Int. J. Medical Informatics.
[10] Ying-Chang Liang,et al. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach , 2019, 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS).
[11] Qiang Yang,et al. Cross-domain sentiment classification via spectral feature alignment , 2010, WWW '10.
[12] Li Huang,et al. Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records , 2019, J. Biomed. Informatics.
[13] Rui Zhang,et al. A Hybrid Approach to Privacy-Preserving Federated Learning , 2018, Informatik Spektrum.
[14] Swaroop Ramaswamy,et al. Federated Learning for Emoji Prediction in a Mobile Keyboard , 2019, ArXiv.
[15] Hongyu Li,et al. An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning , 2019, 2019 Data Compression Conference (DCC).
[16] Xin Qin,et al. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare , 2019, IEEE Intelligent Systems.
[17] Long Hu,et al. Privacy-aware service placement for mobile edge computing via federated learning , 2019, Inf. Sci..
[18] Joseph Dureau,et al. Federated Learning for Keyword Spotting , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[19] Xiaoyan Sun,et al. Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[20] Lifeng Sun,et al. Two-Stream Federated Learning: Reduce the Communication Costs , 2018, 2018 IEEE Visual Communications and Image Processing (VCIP).
[21] Georgios Spathoulas,et al. Machine Learning for All: A More Robust Federated Learning Framework , 2019, ICISSP.
[22] Kin K. Leung,et al. Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.
[23] Vitaly Shmatikov,et al. How To Backdoor Federated Learning , 2018, AISTATS.
[24] Bing Chen,et al. Poisoning Attack in Federated Learning using Generative Adversarial Nets , 2019, 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).
[25] Tianjian Chen,et al. Abnormal Client Behavior Detection in Federated Learning , 2019, ArXiv.
[26] Walid Saad,et al. Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks , 2018, IEEE Transactions on Wireless Communications.
[27] Prateek Mittal,et al. Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.
[28] Albert Y. Zomaya,et al. Federated Learning over Wireless Networks: Optimization Model Design and Analysis , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[29] Richard Nock,et al. Entity Resolution and Federated Learning get a Federated Resolution , 2018, ArXiv.
[30] Tom Ouyang,et al. Federated Learning Of Out-Of-Vocabulary Words , 2019, ArXiv.
[31] Choong Seon Hong,et al. Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).
[32] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[33] Tianjian Chen,et al. A Secure Federated Transfer Learning Framework , 2020, IEEE Intelligent Systems.
[34] Yunus Sarikaya,et al. Motivating Workers in Federated Learning: A Stackelberg Game Perspective , 2019, IEEE Networking Letters.
[35] Tianjian Chen,et al. Federated Machine Learning: Concept and Applications , 2019 .
[36] Amir Houmansadr,et al. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[37] Runhua Xu,et al. HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning , 2019, AISec@CCS.
[38] Tassilo Klein,et al. Differentially Private Federated Learning: A Client Level Perspective , 2017, ArXiv.
[39] Yan Zhang,et al. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics , 2020, IEEE Transactions on Industrial Informatics.
[40] Yang Song,et al. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning , 2018, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[41] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[42] Jiang Du,et al. Electronic medical record security sharing model based on blockchain , 2019, ICCSP.
[43] Dmitriy Dligach,et al. Two-stage Federated Phenotyping and Patient Representation Learning , 2019, BioNLP@ACL.
[44] Stephen A. Jarvis,et al. SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead , 2019, IEEE Transactions on Computers.
[45] Haomiao Yang,et al. Towards Efficient and Privacy-Preserving Federated Deep Learning , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[46] Haoran Yu,et al. Visual Inspection with Federated Learning , 2019, ICIAR.
[47] Seunghak Lee,et al. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server , 2013, NIPS.
[48] Huadong Ma,et al. Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).
[49] Mehryar Mohri,et al. Agnostic Federated Learning , 2019, ICML.
[50] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[51] Yi Zhou,et al. Towards Taming the Resource and Data Heterogeneity in Federated Learning , 2019, OpML.
[52] Huzefa Rangwala,et al. Asynchronous Online Federated Learning for Edge Devices , 2019, ArXiv.
[53] Kejiang Ye,et al. FFD: A Federated Learning Based Method for Credit Card Fraud Detection , 2019, BigData.
[54] Sébastien Gambs,et al. IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning , 2019, 2019 IEEE Security and Privacy Workshops (SPW).
[55] Ameet Talwalkar,et al. Federated Multi-Task Learning , 2017, NIPS.
[56] Yaochu Jin,et al. Multi-Objective Evolutionary Federated Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[57] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..
[58] Mariana Raykova,et al. Privacy-Preserving Distributed Linear Regression on High-Dimensional Data , 2017, Proc. Priv. Enhancing Technol..
[59] Richard Nock,et al. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption , 2017, ArXiv.
[60] Kenneth T. Co,et al. Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging , 2019, ArXiv.
[61] Yasaman Khazaeni,et al. Bayesian Nonparametric Federated Learning of Neural Networks , 2019, ICML.
[62] Yue Zhao,et al. Federated Learning with Non-IID Data , 2018, ArXiv.
[63] Zhu Han,et al. Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism , 2019, IEEE Communications Magazine.
[64] Michael Naehrig,et al. Private Predictive Analysis on Encrypted Medical Data , 2014, IACR Cryptol. ePrint Arch..
[65] Yang Liu,et al. Secure Federated Transfer Learning , 2018, ArXiv.
[66] Tianjian Chen,et al. HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography , 2019, ArXiv.