DDLPF: A Practical Decentralized Deep Learning Paradigm for Internet-of-Things Applications

[1]  Vitaly Shmatikov,et al.  Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[2]  Gerald J. Sussman,et al.  Sparse Representations for Fast, One-Shot Learning , 1997, AAAI/IAAI.

[3]  Ιωάννης Μανώλης,et al.  Οδηγός για το Raspberry Pi 3 Model B , 2017 .

[4]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[5]  Qiang Huo,et al.  Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[7]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[8]  Fei Chen,et al.  Federated Meta-Learning with Fast Convergence and Efficient Communication , 2018 .

[9]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[10]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

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

[13]  Jeffrey Li,et al.  Differentially Private Meta-Learning , 2020, ICLR.

[14]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[15]  Daniel Davis Wood,et al.  ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER , 2014 .

[16]  Gaurav Kapoor,et al.  Protection Against Reconstruction and Its Applications in Private Federated Learning , 2018, ArXiv.

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

[18]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.

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

[20]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[21]  Hao Wen,et al.  Distributing Deep Neural Networks with Containerized Partitions at the Edge , 2019, HotEdge.

[22]  Pradeep Dubey,et al.  Distributed Deep Learning Using Synchronous Stochastic Gradient Descent , 2016, ArXiv.

[23]  Silvio Micali,et al.  Algorand: Scaling Byzantine Agreements for Cryptocurrencies , 2017, IACR Cryptol. ePrint Arch..

[24]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[25]  Silvio Micali,et al.  Verifiable random functions , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

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

[27]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[28]  Hubert Eichner,et al.  Towards Federated Learning at Scale: System Design , 2019, SysML.

[29]  H. Vincent Poor,et al.  Federated Learning With Differential Privacy: Algorithms and Performance Analysis , 2019, IEEE Transactions on Information Forensics and Security.

[30]  Vitalik Buterin.  A NEXT GENERATION SMART CONTRACT & DECENTRALIZED APPLICATION PLATFORM , 2015 .

[31]  Yue Zhao,et al.  Federated Learning with Non-IID Data , 2018, ArXiv.

[32]  Andrea Cavallaro,et al.  Protecting Sensory Data against Sensitive Inferences , 2018, P2DS@EuroSys.

[33]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[34]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[35]  Rui Zhang,et al.  A Hybrid Approach to Privacy-Preserving Federated Learning , 2018, Informatik Spektrum.

[36]  Joshua Achiam,et al.  On First-Order Meta-Learning Algorithms , 2018, ArXiv.

[37]  Ivan Damgård,et al.  A Generalisation, a Simplification and Some Applications of Paillier's Probabilistic Public-Key System , 2001, Public Key Cryptography.

[38]  J. Schulman,et al.  Reptile: a Scalable Metalearning Algorithm , 2018 .

[39]  Joshua B. Tenenbaum,et al.  Human-level concept learning through probabilistic program induction , 2015, Science.