A survey on federated learning

[1]  Maoguo Gong,et al.  A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2020, Frontiers in Neurorobotics.

[2]  Pim Tuyls,et al.  Efficient Binary Conversion for Paillier Encrypted Values , 2006, EUROCRYPT.

[3]  James J. Q. Yu,et al.  Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach , 2020, IEEE Internet of Things Journal.

[4]  Monica Nicoli,et al.  Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks , 2019, IEEE Internet of Things Journal.

[5]  Alexander J. Smola,et al.  Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.

[6]  Maoguo Gong,et al.  A Survey on Differentially Private Machine Learning [Review Article] , 2020, IEEE Computational Intelligence Magazine.

[7]  Stratis Ioannidis,et al.  Privacy-Preserving Ridge Regression on Hundreds of Millions of Records , 2013, 2013 IEEE Symposium on Security and Privacy.

[8]  Seunghak Lee,et al.  More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server , 2013, NIPS.

[9]  Mariana Raykova,et al.  Secure Linear Regression on Vertically Partitioned Datasets , 2016, IACR Cryptol. ePrint Arch..

[10]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[11]  Ameet Talwalkar,et al.  Federated Multi-Task Learning , 2017, NIPS.

[12]  Klaus-Robert Müller,et al.  Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Zibin Zheng,et al.  Blockchain challenges and opportunities: a survey , 2018, Int. J. Web Grid Serv..

[14]  Yanjiao Chen,et al.  InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[15]  Tony Q. S. Quek,et al.  On Safeguarding Privacy and Security in the Framework of Federated Learning , 2019, IEEE Network.

[16]  Nathan Srebro,et al.  Semi-Cyclic Stochastic Gradient Descent , 2019, ICML.

[17]  Yehuda Lindell,et al.  A Proof of Security of Yao’s Protocol for Two-Party Computation , 2009, Journal of Cryptology.

[18]  Shiho Moriai,et al.  Privacy-Preserving Deep Learning via Additively Homomorphic Encryption , 2018, IEEE Transactions on Information Forensics and Security.

[19]  Yasaman Khazaeni,et al.  Bayesian Nonparametric Federated Learning of Neural Networks , 2019, ICML.

[20]  Mohsen Guizani,et al.  Reliable Federated Learning for Mobile Networks , 2019, IEEE Wireless Communications.

[21]  Qiang Yang,et al.  Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..

[22]  Jan Philipp Albrecht,et al.  How the GDPR Will Change the World , 2016 .

[23]  Jerome P. Reiter,et al.  Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products , 2009 .

[24]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

[25]  Whitmore Gray,et al.  General Principles of Civil Law of the People's Republic of China , 1986 .

[26]  Maoguo Gong,et al.  Privacy-enhanced multi-party deep learning , 2020, Neural Networks.

[27]  Sanjiv Kumar,et al.  Federated Learning with Only Positive Labels , 2020, ICML.

[28]  Frank Meng,et al.  From Discovery to Practice and Survivorship: Building a National Real‐World Data Learning Healthcare Framework for Military and Veteran Cancer Patients , 2019, Clinical pharmacology and therapeutics.

[29]  Sanjiv Kumar,et al.  cpSGD: Communication-efficient and differentially-private distributed SGD , 2018, NeurIPS.

[30]  Shashi Raj Pandey,et al.  A Crowdsourcing Framework for On-Device Federated Learning , 2019, IEEE Transactions on Wireless Communications.

[31]  Zhi Ding,et al.  Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.

[32]  Wenliang Du,et al.  Privacy-preserving cooperative statistical analysis , 2001, Seventeenth Annual Computer Security Applications Conference.

[33]  Li Chen,et al.  Robust Federated Learning With Noisy Communication , 2019, IEEE Transactions on Communications.

[34]  W. Price,et al.  Privacy in the age of medical big data , 2019, Nature Medicine.

[35]  Somesh Jha,et al.  Privacy-Preserving Ridge Regression on Distributed Data , 2017, IACR Cryptol. ePrint Arch..

[36]  Maria-Florina Balcan,et al.  Adaptive Gradient-Based Meta-Learning Methods , 2019, NeurIPS.

[37]  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).

[38]  Omid Semiari,et al.  Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[39]  Max Parasol,et al.  The impact of China's 2016 Cyber Security Law on foreign technology firms, and on China's big data and Smart City dreams , 2017, Comput. Law Secur. Rev..

[40]  Bo Yang,et al.  Customized Federated Learning for accelerated edge computing with heterogeneous task targets , 2020, Comput. Networks.

[41]  Deniz Gündüz,et al.  Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[42]  Michael I. Jordan,et al.  Estimation, Optimization, and Parallelism when Data is Sparse , 2013, NIPS.

[43]  Tianjian Chen,et al.  A Secure Federated Transfer Learning Framework , 2020, IEEE Intelligent Systems.

[44]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[45]  Deniz Gündüz,et al.  Federated Learning Over Wireless Fading Channels , 2019, IEEE Transactions on Wireless Communications.

[46]  Sarvar Patel,et al.  Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..

[47]  Yunghsiang Sam Han,et al.  Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification , 2004, SDM.

[48]  Chen Zhang,et al.  Secure collaborative few-shot learning , 2020, Knowl. Based Syst..

[49]  Shenghuo Zhu,et al.  Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning , 2018, AAAI.

[50]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[51]  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).

[52]  Amir Salman Avestimehr,et al.  Coded Computation Over Heterogeneous Clusters , 2019, IEEE Transactions on Information Theory.

[53]  H. Brendan McMahan,et al.  Learning Differentially Private Recurrent Language Models , 2017, ICLR.

[54]  Georges Kaddoum,et al.  Lightwave Power Transfer for Federated Learning-Based Wireless Networks , 2020, IEEE Communications Letters.

[55]  Raef Bassily,et al.  Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds , 2014, 1405.7085.

[56]  Dimitris S. Papailiopoulos,et al.  Perturbed Iterate Analysis for Asynchronous Stochastic Optimization , 2015, SIAM J. Optim..