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110 lines
3.8 KiB
BibTeX
110 lines
3.8 KiB
BibTeX
@inproceedings{fedavg,
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author = {Brendan McMahan and
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Eider Moore and
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Daniel Ramage and
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Seth Hampson and
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Blaise Ag{\"{u}}era y Arcas},
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title = {Communication-Efficient Learning of Deep Networks from Decentralized
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Data},
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booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence
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and Statistics, {AISTATS} 2017, 20-22 April 2017, Fort Lauderdale,
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FL, {USA}},
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series = {Proceedings of Machine Learning Research},
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volume = {54},
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pages = {1273--1282},
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publisher = {{PMLR}},
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year = {2017},
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url = {http://proceedings.mlr.press/v54/mcmahan17a.html},
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}
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@inproceedings{scaffold,
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title={Scaffold: Stochastic controlled averaging for federated learning},
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author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha},
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booktitle={International Conference on Machine Learning},
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pages={5132--5143},
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year={2020},
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organization={PMLR}
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}
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@article{FedDF,
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title={Ensemble distillation for robust model fusion in federated learning},
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author={Lin, Tao and Kong, Lingjing and Stich, Sebastian U and Jaggi, Martin},
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journal={Advances in Neural Information Processing Systems},
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volume={33},
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pages={2351--2363},
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year={2020}
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}
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@inproceedings{FedBE,
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author = {Hong{-}You Chen and
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Wei{-}Lun Chao},
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title = {FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning},
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booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
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Virtual Event, Austria, May 3-7, 2021},
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year = {2021},
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}
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@article{FedAvg_Momentum,
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author = {Tzu{-}Ming Harry Hsu and
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Hang Qi and
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Matthew Brown},
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title = {Measuring the Effects of Non-Identical Data Distribution for Federated
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Visual Classification},
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journal = {CoRR},
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volume = {abs/1909.06335},
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year = {2019},
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url = {http://arxiv.org/abs/1909.06335},
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eprinttype = {arXiv},
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eprint = {1909.06335},
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}
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@inproceedings{PATE,
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author = {Nicolas Papernot and
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Mart{\'{\i}}n Abadi and
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{\'{U}}lfar Erlingsson and
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Ian J. Goodfellow and
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Kunal Talwar},
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title = {Semi-supervised Knowledge Transfer for Deep Learning from Private
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Training Data},
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booktitle = {5th International Conference on Learning Representations, {ICLR} 2017,
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Toulon, France, April 24-26, 2017, Conference Track Proceedings},
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publisher = {OpenReview.net},
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year = {2017},
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}
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@inproceedings{FedProx,
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title={Federated optimization in heterogeneous networks},
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author={Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},
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booktitle={Proceedings of Machine Learning and Systems},
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volume={2},
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pages={429--450},
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year={2020}
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}
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@article{jiang2022signds,
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title={SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection},
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author={Jiang, Xue and Zhou, Xuebing and Grossklags, Jens},
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journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
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year={2022},
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publisher = {Association for Computing Machinery},
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address = {New York, USA}
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}
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@inproceedings{mcsherry2007mechanism,
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title={Mechanism design via differential privacy},
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author={McSherry, Frank and Talwar, Kunal},
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booktitle={IEEE Symposium on Foundations of Computer Science},
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pages={94--103},
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year={2007},
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}
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@article{dwork2014algorithmic,
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title={The algorithmic foundations of differential privacy},
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author={Dwork, Cynthia and Roth, Aaron},
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journal={Foundations and Trends in Theoretical Computer Science},
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volume={9},
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number={3--4},
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pages={211--407},
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year={2014},
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}
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