QRES/docs/references.bib at main · CavinKrenik/QRES · GitHub

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% QRES References
% BibTeX entries for paper submission

@inproceedings{mcmahan2017fedavg,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@inproceedings{li2020fedprox,
  title={Federated optimization in heterogeneous networks},
  author={Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},
  booktitle={MLSys},
  year={2020}
}

@inproceedings{bonawitz2017secagg,
  title={Practical secure aggregation for privacy-preserving machine learning},
  author={Bonawitz, Keith and Ivanov, Vladimir and Kreuter, Ben and Marcedone, Antonio and McMahan, H Brendan and Patel, Sarvar and Ramage, Daniel and Segal, Aaron and Seth, Karn},
  booktitle={CCS},
  pages={1175--1191},
  year={2017}
}

@inproceedings{blanchard2017krum,
  title={Machine learning with adversaries: Byzantine tolerant gradient descent},
  author={Blanchard, Peva and El Mhamdi, El Mahdi and Guerraoui, Rachid and Stainer, Julien},
  booktitle={NeurIPS},
  pages={119--129},
  year={2017}
}

@inproceedings{abadi2016dpsgd,
  title={Deep learning with differential privacy},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={CCS},
  pages={308--318},
  year={2016}
}

@article{dwork2014dp,
  title={The algorithmic foundations of differential privacy},
  author={Dwork, Cynthia and Roth, Aaron},
  journal={Foundations and Trends in Theoretical Computer Science},
  volume={9},
  number={3-4},
  pages={211--407},
  year={2014}
}

@article{maass1997snn,
  title={Networks of spiking neurons: the third generation of neural network models},
  author={Maass, Wolfgang},
  journal={Neural networks},
  volume={10},
  number={9},
  pages={1659--1671},
  year={1997}
}

@article{pfeiffer2018neuromorphic,
  title={Deep learning with spiking neurons: opportunities and challenges},
  author={Pfeiffer, Michael and Pfeil, Thomas},
  journal={Frontiers in neuroscience},
  volume={12},
  pages={774},
  year={2018}
}

@inproceedings{yin2018trimmed,
  title={Byzantine-robust distributed learning: Towards optimal statistical rates},
  author={Yin, Dong and Chen, Yudong and Kannan, Ramchandran and Bartlett, Peter},
  booktitle={ICML},
  pages={5650--5659},
  year={2018}
}

@article{kairouz2019advances,
  title={Advances and open problems in federated learning},
  author={Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and others},
  journal={arXiv preprint arXiv:1912.04977},
  year={2019}
}

@inproceedings{geyer2017differentially,
  title={Differentially private federated learning: A client level perspective},
  author={Geyer, Robin C and Klein, Tassilo and Nabi, Moin},
  booktitle={NeurIPS Workshop on Privacy in ML},
  year={2017}
}

@article{bell2020secagg,
  title={Secure single-server aggregation with (poly) logarithmic overhead},
  author={Bell, James Henry and Bonawitz, Kallista A and Gascón, Adrià and Lepoint, Tancrède and Raykova, Mariana},
  journal={CCS},
  year={2020}
}