diff --git a/mlsys.bib b/mlsys.bib index b581186..76c37af 100644 --- a/mlsys.bib +++ b/mlsys.bib @@ -652,89 +652,6 @@ location = {Halifax, NS, Canada}, series = {ADKDD'17} } -@inproceedings{fedavg, - author = {Brendan McMahan and - Eider Moore and - Daniel Ramage and - Seth Hampson and - Blaise Ag{\"{u}}era y Arcas}, - title = {Communication-Efficient Learning of Deep Networks from Decentralized - Data}, - booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence - and Statistics, {AISTATS} 2017, 20-22 April 2017, Fort Lauderdale, - FL, {USA}}, - series = {Proceedings of Machine Learning Research}, - volume = {54}, - pages = {1273--1282}, - publisher = {{PMLR}}, - year = {2017}, - url = {http://proceedings.mlr.press/v54/mcmahan17a.html}, -} - -@inproceedings{scaffold, - title={Scaffold: Stochastic controlled averaging for federated learning}, - author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha}, - booktitle={International Conference on Machine Learning}, - pages={5132--5143}, - year={2020}, - organization={PMLR} -} - -@article{FedDF, - title={Ensemble distillation for robust model fusion in federated learning}, - author={Lin, Tao and Kong, Lingjing and Stich, Sebastian U and Jaggi, Martin}, - journal={Advances in Neural Information Processing Systems}, - volume={33}, - pages={2351--2363}, - year={2020} -} - -@inproceedings{PATE, - author = {Nicolas Papernot and - Mart{\'{\i}}n Abadi and - {\'{U}}lfar Erlingsson and - Ian J. Goodfellow and - Kunal Talwar}, - title = {Semi-supervised Knowledge Transfer for Deep Learning from Private - Training Data}, - booktitle = {5th International Conference on Learning Representations, {ICLR} 2017, - Toulon, France, April 24-26, 2017, Conference Track Proceedings}, - publisher = {OpenReview.net}, - year = {2017}, -} - -@inproceedings{FedProx, - 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={Proceedings of Machine Learning and Systems}, - volume={2}, - pages={429--450}, - year={2020} -} - -@inproceedings{FedBE, - author = {Hong{-}You Chen and - Wei{-}Lun Chao}, - title = {FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning}, - booktitle = {9th International Conference on Learning Representations, {ICLR} 2021, - Virtual Event, Austria, May 3-7, 2021}, - year = {2021}, -} - -@article{FedAvg_Momentum, - author = {Tzu{-}Ming Harry Hsu and - Hang Qi and - Matthew Brown}, - title = {Measuring the Effects of Non-Identical Data Distribution for Federated - Visual Classification}, - journal = {CoRR}, - volume = {abs/1909.06335}, - year = {2019}, - url = {http://arxiv.org/abs/1909.06335}, - eprinttype = {arXiv}, - eprint = {1909.06335}, -} - @inproceedings{ijcai2017-239, author = {Huifeng Guo and Ruiming TANG and Yunming Ye and Zhenguo Li and Xiuqiang He}, title = {DeepFM: A Factorization-Machine based Neural Network for CTR Prediction}, @@ -833,33 +750,6 @@ series = {PPoPP '21} abstract = "Transformer and its variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for real industrial applications. In this paper, we propose , a highly efficient inference library for models in the Transformer family. includes a series of GPU optimization techniques to both streamline the computation of Transformer layers and reduce memory footprint. supports models trained using PyTorch and Tensorflow. Experimental results on standard machine translation benchmarks show that achieves up to 14x speedup compared with TensorFlow and 1.4x speedup compared with , a concurrent CUDA implementation. The code will be released publicly after the review.", } -@article{jiang2022signds, - title={SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection}, - author={Jiang, Xue and Zhou, Xuebing and Grossklags, Jens}, - journal={ACM Transactions on Intelligent Systems and Technology (TIST)}, - year={2022}, - publisher = {Association for Computing Machinery}, - address = {New York, USA} -} - -@article{dwork2014algorithmic, - 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}, -} - -@inproceedings{mcsherry2007mechanism, - title={Mechanism design via differential privacy}, - author={McSherry, Frank and Talwar, Kunal}, - booktitle={IEEE Symposium on Foundations of Computer Science}, - pages={94--103}, - year={2007}, -} - @inproceedings{quigley2009ros, title={ROS: an open-source Robot Operating System}, author={Quigley, Morgan and Conley, Ken and Gerkey, Brian and Faust, Josh and Foote, Tully and Leibs, Jeremy and Wheeler, Rob and Ng, Andrew Y and others}, @@ -1390,4 +1280,4 @@ series = {EuroSys '22} author={Jiankai Sun and Shreyas Kousik and David Fridovich-Keil and Mac Schwager}, journal={arXiv preprint}, year={2022} -} \ No newline at end of file +} diff --git a/references/federated.bib b/references/federated.bib index e69de29..d73fdbc 100644 --- a/references/federated.bib +++ b/references/federated.bib @@ -0,0 +1,109 @@ +@inproceedings{fedavg, + author = {Brendan McMahan and + Eider Moore and + Daniel Ramage and + Seth Hampson and + Blaise Ag{\"{u}}era y Arcas}, + title = {Communication-Efficient Learning of Deep Networks from Decentralized + Data}, + booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence + and Statistics, {AISTATS} 2017, 20-22 April 2017, Fort Lauderdale, + FL, {USA}}, + series = {Proceedings of Machine Learning Research}, + volume = {54}, + pages = {1273--1282}, + publisher = {{PMLR}}, + year = {2017}, + url = {http://proceedings.mlr.press/v54/mcmahan17a.html}, +} + +@inproceedings{scaffold, + title={Scaffold: Stochastic controlled averaging for federated learning}, + author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha}, + booktitle={International Conference on Machine Learning}, + pages={5132--5143}, + year={2020}, + organization={PMLR} +} + +@article{FedDF, + title={Ensemble distillation for robust model fusion in federated learning}, + author={Lin, Tao and Kong, Lingjing and Stich, Sebastian U and Jaggi, Martin}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={2351--2363}, + year={2020} +} + +@inproceedings{FedBE, + author = {Hong{-}You Chen and + Wei{-}Lun Chao}, + title = {FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning}, + booktitle = {9th International Conference on Learning Representations, {ICLR} 2021, + Virtual Event, Austria, May 3-7, 2021}, + year = {2021}, +} + +@article{FedAvg_Momentum, + author = {Tzu{-}Ming Harry Hsu and + Hang Qi and + Matthew Brown}, + title = {Measuring the Effects of Non-Identical Data Distribution for Federated + Visual Classification}, + journal = {CoRR}, + volume = {abs/1909.06335}, + year = {2019}, + url = {http://arxiv.org/abs/1909.06335}, + eprinttype = {arXiv}, + eprint = {1909.06335}, +} + +@inproceedings{PATE, + author = {Nicolas Papernot and + Mart{\'{\i}}n Abadi and + {\'{U}}lfar Erlingsson and + Ian J. Goodfellow and + Kunal Talwar}, + title = {Semi-supervised Knowledge Transfer for Deep Learning from Private + Training Data}, + booktitle = {5th International Conference on Learning Representations, {ICLR} 2017, + Toulon, France, April 24-26, 2017, Conference Track Proceedings}, + publisher = {OpenReview.net}, + year = {2017}, +} + +@inproceedings{FedProx, + 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={Proceedings of Machine Learning and Systems}, + volume={2}, + pages={429--450}, + year={2020} +} + +@article{jiang2022signds, + title={SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection}, + author={Jiang, Xue and Zhou, Xuebing and Grossklags, Jens}, + journal={ACM Transactions on Intelligent Systems and Technology (TIST)}, + year={2022}, + publisher = {Association for Computing Machinery}, + address = {New York, USA} +} + +@inproceedings{mcsherry2007mechanism, + title={Mechanism design via differential privacy}, + author={McSherry, Frank and Talwar, Kunal}, + booktitle={IEEE Symposium on Foundations of Computer Science}, + pages={94--103}, + year={2007}, +} + +@article{dwork2014algorithmic, + 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}, +}