cs249r_book/contents/ondevice_learning/ondevice_learning.bib

@inproceedings{abadi2016deep,
 address = {New York, NY, USA},
 author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
 booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security},
 date-added = {2023-11-22 18:06:03 -0500},
 date-modified = {2023-11-22 18:08:42 -0500},
 doi = {10.1145/2976749.2978318},
 keywords = {deep learning, differential privacy},
 pages = {308--318},
 publisher = {ACM},
 series = {CCS '16},
 source = {Crossref},
 title = {Deep Learning with Differential Privacy},
 url = {https://doi.org/10.1145/2976749.2978318},
 year = {2016}
}

@inproceedings{cai2020tinytl,
 author = {Han Cai and Chuang Gan and Ligeng Zhu and Song Han},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/CaiGZ020.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual},
 editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning},
 url = {https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html},
 year = {2020}
}

@article{chen2016training,
 author = {Chen, Tianqi and Xu, Bing and Zhang, Chiyuan and Guestrin, Carlos},
 journal = {ArXiv preprint},
 title = {Training deep nets with sublinear memory cost},
 url = {https://arxiv.org/abs/1604.06174},
 volume = {abs/1604.06174},
 year = {2016}
}

@inproceedings{chen2018tvm,
 author = {Chen, Tianqi and Moreau, Thierry and Jiang, Ziheng and Zheng, Lianmin and Yan, Eddie and Shen, Haichen and Cowan, Meghan and Wang, Leyuan and Hu, Yuwei and Ceze, Luis and others},
 booktitle = {13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)},
 pages = {578--594},
 title = {{TVM:} {An} automated End-to-End optimizing compiler for deep learning},
 year = {2018}
}

@article{chen2023learning,
 author = {Chen, Zhiyong and Xu, Shugong},
 doi = {10.1186/s13636-023-00299-2},
 issn = {1687-4722},
 journal = {EURASIP Journal on Audio, Speech, and Music Processing},
 number = {1},
 pages = {33},
 publisher = {Springer Science and Business Media LLC},
 source = {Crossref},
 title = {Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning},
 url = {https://doi.org/10.1186/s13636-023-00299-2},
 volume = {2023},
 year = {2023}
}

@article{david2021tensorflow,
 author = {David, Robert and Duke, Jared and Jain, Advait and Janapa Reddi, Vijay and Jeffries, Nat and Li, Jian and Kreeger, Nick and Nappier, Ian and Natraj, Meghna and Wang, Tiezhen and others},
 journal = {Proceedings of Machine Learning and Systems},
 pages = {800--811},
 title = {Tensorflow lite micro: {Embedded} machine learning for tinyml systems},
 volume = {3},
 year = {2021}
}

@article{desai2016five,
 author = {Desai, Tanvi and Ritchie, Felix and Welpton, Richard and others},
 journal = {Economics Working Paper Series},
 pages = {28},
 title = {Five Safes: {Designing} data access for research},
 volume = {1601},
 year = {2016}
}

@article{dhar2021survey,
 author = {Dhar, Sauptik and Guo, Junyao and Liu, Jiayi (Jason) and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak},
 doi = {10.1145/3450494},
 issn = {2691-1914, 2577-6207},
 journal = {ACM Transactions on Internet of Things},
 number = {3},
 pages = {1--49},
 publisher = {Association for Computing Machinery (ACM)},
 source = {Crossref},
 subtitle = {An Algorithms and Learning Theory Perspective},
 title = {A Survey of On-Device Machine Learning},
 url = {https://doi.org/10.1145/3450494},
 volume = {2},
 year = {2021}
}

@article{dwork2014algorithmic,
 author = {Dwork, Cynthia and Roth, Aaron},
 doi = {10.1561/0400000042},
 issn = {1551-305X, 1551-3068},
 journal = {Foundations and Trends{\textregistered} in Theoretical Computer Science},
 number = {3-4},
 pages = {211--407},
 publisher = {Now Publishers},
 source = {Crossref},
 title = {The Algorithmic Foundations of Differential Privacy},
 url = {https://doi.org/10.1561/0400000042},
 volume = {9},
 year = {2013}
}

@article{esteva2017dermatologist,
 author = {Esteva, Andre and Kuprel, Brett and Novoa, Roberto A. and Ko, Justin and Swetter, Susan M. and Blau, Helen M. and Thrun, Sebastian},
 doi = {10.1038/nature21056},
 issn = {0028-0836, 1476-4687},
 journal = {Nature},
 number = {7639},
 pages = {115--118},
 publisher = {Springer Science and Business Media LLC},
 source = {Crossref},
 title = {Dermatologist-level classification of skin cancer with deep neural networks},
 url = {https://doi.org/10.1038/nature21056},
 volume = {542},
 year = {2017}
}

@inproceedings{gruslys2016memory,
 author = {Audrunas Gruslys and R{\'{e}}mi Munos and Ivo Danihelka and Marc Lanctot and Alex Graves},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/GruslysMDLG16.bib},
 booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain},
 editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike von Luxburg and Isabelle Guyon and Roman Garnett},
 pages = {4125--4133},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Memory-Efficient Backpropagation Through Time},
 url = {https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html},
 year = {2016}
}

@inproceedings{hong2023publishing,
 author = {Hong, Sanghyun and Carlini, Nicholas and Kurakin, Alexey},
 booktitle = {2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},
 doi = {10.1109/satml54575.2023.00026},
 organization = {IEEE},
 pages = {271--290},
 publisher = {IEEE},
 source = {Crossref},
 title = {Publishing Efficient On-device Models Increases Adversarial Vulnerability},
 url = {https://doi.org/10.1109/satml54575.2023.00026},
 year = {2023}
}

@inproceedings{kairouz2015secure,
 author = {Peter Kairouz and Sewoong Oh and Pramod Viswanath},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/KairouzOV15.bib},
 booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada},
 editor = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and Masashi Sugiyama and Roman Garnett},
 pages = {2008--2016},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Secure Multi-party Differential Privacy},
 url = {https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html},
 year = {2015}
}

@article{karargyris2023federated,
 author = {Karargyris, Alexandros and Umeton, Renato and Sheller, Micah J and Aristizabal, Alejandro and George, Johnu and Wuest, Anna and Pati, Sarthak and Kassem, Hasan and Zenk, Maximilian and Baid, Ujjwal and others},
 doi = {10.1038/s42256-023-00652-2},
 issn = {2522-5839},
 journal = {Nature Machine Intelligence},
 number = {7},
 pages = {799--810},
 publisher = {Springer Science and Business Media LLC},
 source = {Crossref},
 title = {Federated benchmarking of medical artificial intelligence with {MedPerf}},
 url = {https://doi.org/10.1038/s42256-023-00652-2},
 volume = {5},
 year = {2023}
}

@article{kwon2023tinytrain,
 author = {Kwon, Young D and Li, Rui and Venieris, Stylianos I and Chauhan, Jagmohan and Lane, Nicholas D and Mascolo, Cecilia},
 journal = {ArXiv preprint},
 title = {{TinyTrain:} {Deep} Neural Network Training at the Extreme Edge},
 url = {https://arxiv.org/abs/2307.09988},
 volume = {abs/2307.09988},
 year = {2023}
}

@inproceedings{li2016lightrnn,
 author = {Xiang Li and Tao Qin and Jian Yang and Tie{-}Yan Liu},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/LiQYHL16.bib},
 booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain},
 editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike von Luxburg and Isabelle Guyon and Roman Garnett},
 pages = {4385--4393},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {LightRNN: Memory and Computation-Efficient Recurrent Neural Networks},
 url = {https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html},
 year = {2016}
}

@inproceedings{lin2020mcunet,
 author = {Ji Lin and Wei{-}Ming Chen and Yujun Lin and John Cohn and Chuang Gan and Song Han},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/LinCLCG020.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual},
 editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin},
 timestamp = {Thu, 11 Feb 2021 00:00:00 +0100},
 title = {MCUNet: Tiny Deep Learning on IoT Devices},
 url = {https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html},
 year = {2020}
}

@article{lin2022device,
 author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song},
 journal = {Adv. Neur. In.},
 pages = {22941--22954},
 title = {On-device training under 256kb memory},
 volume = {35},
 year = {2022}
}

@article{moshawrab2023reviewing,
 author = {Moshawrab, Mohammad and Adda, Mehdi and Bouzouane, Abdenour and Ibrahim, Hussein and Raad, Ali},
 doi = {10.3390/electronics12102287},
 issn = {2079-9292},
 journal = {Electronics},
 number = {10},
 pages = {2287},
 publisher = {MDPI AG},
 source = {Crossref},
 title = {Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives},
 url = {https://doi.org/10.3390/electronics12102287},
 volume = {12},
 year = {2023}
}

@inproceedings{nguyen2023re,
 author = {Nguyen, Ngoc-Bao and Chandrasegaran, Keshigeyan and Abdollahzadeh, Milad and Cheung, Ngai-Man},
 booktitle = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 doi = {10.1109/cvpr52729.2023.01572},
 pages = {16384--16393},
 publisher = {IEEE},
 source = {Crossref},
 title = {Re-Thinking Model Inversion Attacks Against Deep Neural Networks},
 url = {https://doi.org/10.1109/cvpr52729.2023.01572},
 year = {2023}
}

@article{pan2009survey,
 author = {Pan, Sinno Jialin and Yang, Qiang},
 doi = {10.1109/tkde.2009.191},
 issn = {1041-4347},
 journal = {IEEE Trans. Knowl. Data Eng.},
 number = {10},
 pages = {1345--1359},
 publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
 source = {Crossref},
 title = {A Survey on Transfer Learning},
 url = {https://doi.org/10.1109/tkde.2009.191},
 volume = {22},
 year = {2010}
}

@inproceedings{rouhani2017tinydl,
 author = {Darvish Rouhani, Bita and Mirhoseini, Azalia and Koushanfar, Farinaz},
 bdsk-url-1 = {https://doi.org/10.1109/ISCAS.2017.8050343},
 booktitle = {2017 IEEE International Symposium on Circuits and Systems (ISCAS)},
 doi = {10.1109/iscas.2017.8050343},
 pages = {1--4},
 publisher = {IEEE},
 source = {Crossref},
 title = {{TinyDL:} {Just-in-time} deep learning solution for constrained embedded systems},
 url = {https://doi.org/10.1109/iscas.2017.8050343},
 year = {2017}
}

@inproceedings{shi2022data,
 author = {Shi, Hongrui and Radu, Valentin},
 booktitle = {Proceedings of the 2nd European Workshop on Machine Learning and Systems},
 doi = {10.1145/3517207.3526980},
 pages = {72--78},
 publisher = {ACM},
 source = {Crossref},
 title = {Data selection for efficient model update in federated learning},
 url = {https://doi.org/10.1145/3517207.3526980},
 year = {2022}
}

@article{wu2022sustainable,
 author = {Wu, Carole-Jean and Raghavendra, Ramya and Gupta, Udit and Acun, Bilge and Ardalani, Newsha and Maeng, Kiwan and Chang, Gloria and Aga, Fiona and Huang, Jinshi and Bai, Charles and others},
 journal = {Proceedings of Machine Learning and Systems},
 pages = {795--813},
 title = {Sustainable ai: {Environmental} implications, challenges and opportunities},
 volume = {4},
 year = {2022}
}

@article{xu2023federated,
 author = {Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A and Kairouz, Peter and McMahan, H Brendan and Rosenstock, Jesse and Zhang, Yuanbo},
 journal = {ArXiv preprint},
 title = {Federated Learning of Gboard Language Models with Differential Privacy},
 url = {https://arxiv.org/abs/2305.18465},
 volume = {abs/2305.18465},
 year = {2023}
}

@inproceedings{yang2023online,
 author = {Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Fran\c{c}oise and Mathews, Rajiv and Chen, Mingqing},
 booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
 doi = {10.1109/icassp49357.2023.10097124},
 organization = {IEEE},
 pages = {1--5},
 publisher = {IEEE},
 source = {Crossref},
 title = {Online Model Compression for Federated Learning with Large Models},
 url = {https://doi.org/10.1109/icassp49357.2023.10097124},
 year = {2023}
}

@article{zhao2018federated,
 author = {Zhao, Yue and Li, Meng and Lai, Liangzhen and Suda, Naveen and Civin, Damon and Chandra, Vikas},
 journal = {ArXiv preprint},
 title = {Federated learning with non-iid data},
 url = {https://arxiv.org/abs/1806.00582},
 volume = {abs/1806.00582},
 year = {2018}
}

@article{zhuang2021comprehensive,
 author={Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing},
 journal={Proceedings of the IEEE}, 
 title={A Comprehensive Survey on Transfer Learning}, 
 year={2021},
 volume={109},
 number={1},
 pages={43-76},
 keywords={Transfer learning;Semisupervised learning;Data models;Covariance matrices;Machine learning;Adaptation models;Domain adaptation;interpretation;machine learning;transfer learning},
 doi={10.1109/JPROC.2020.3004555}
}

@inproceedings{cai2020tinytl,    
  title         = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning},    
  author        = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song},
  booktitle     = {Advances in Neural Information Processing Systems},  
  volume        = {33},
  year          = {2020}
}