{"type":"ScholarlyArticle","title":"Reducing Communication for Split Learning by Randomized Top-k Sparsification","authors":[{"name":"Fei Zheng","slug":"fei-zheng","given":"Fei","family":"Zheng","affiliation":"Zhejiang University"},{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":"Zhejiang University"},{"name":"Lingjuan Lyu","slug":"lingjuan-lyu","given":"Lingjuan","family":"Lyu","affiliation":"Sony AI"},{"name":"Binhui Yao","slug":"binhui-yao","given":"Binhui","family":"Yao","affiliation":"Midea Group"}],"abstract":"Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model generalize and converge better. This is done by selecting top-k elements with a large probability while also having a small probability to select non-top-k elements. Empirical results show that compared with other communication-reduction methods, our proposed randomized top-k sparsification achieves a better model performance under the same compression level.","doi":"10.24963/ijcai.2023/519","url":"https://ijcai.org/proceedings/2023/519","pdf":"https://ijcai.org/proceedings/2023/0519.pdf","markdown":"https://ijcai.org/proceedings/2023/519.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/519","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":519,"pages":{"first":4665,"last":4673},"keywords":["Multidisciplinary Topics and Applications: MDA: Security and privacy","Machine Learning: ML: Learning sparse models","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Aji and Heafield, 2017] Alham Fikri Aji and Kenneth Heafield. Sparse communication for distributed gradient descent. In Proceedings of the 2017 Conference on Em- pirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, pages 440-445. Association for Computational Linguistics, 2017.","title":"Sparse communication for distributed gradient descent","authors":"Heafield ; Aji, Fikri Alham, Kenneth Aji, Heafield","year":"2017","venue":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Kobi- larov, Jens Lehmann, Richard Cyganiak, and Zachary G. Ives. Dbpedia: A nucleus for a web of open data. In The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, volume 4825 of Lecture Notes in Computer Science, pages 722-735. Springer, 2007.","title":"Dbpedia: A nucleus for a web of open data","authors":"Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, Zachary G Ives","year":"2007","venue":"The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ayad et al., 2021] Ahmad Ayad, Melvin Renner, and Anke Schmeink. Improving the communication and computation efficiency of split learning for iot applications. In IEEE Global Communications Conference, GLOBECOM 2021, Madrid, Spain, December 7-11, 2021, pages 1-6. IEEE, 2021.","title":"Improving the communication and computation efficiency of split learning for iot applications","authors":"Ahmad Ayad, Melvin Renner, Anke Schmeink","year":"2021","venue":"IEEE Global Communications Conference, GLOBECOM 2021","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ben-Shimon et al., 2015] David Ben-Shimon, Alexander Tsikinovsky, Michael Friedmann, Bracha Shapira, Lior Rokach, and Johannes Hoerle. Recsys challenge 2015 and the yoochoose dataset. In Proceedings of the 9th ACM Con- ference on Recommender Systems, pages 357-358, 2015.","title":"Recsys challenge 2015 and the yoochoose dataset","authors":"David Ben-Shimon, Alexander Tsikinovsky, Michael Friedmann, Bracha Shapira, Lior Rokach, Johannes Hoerle","year":"2015","venue":"Proceedings of the 9th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Castiglia et al., 2022] Timothy J. Castiglia, Anirban Das, Shiqiang Wang, and Stacy Patterson. Compressed-vfl: Communication-efficient learning with vertically parti- tioned data. In International Conference on Machine Learn- ing, ICML 2022, volume 162 of Proceedings of Machine Learning Research, pages 2738-2766. PMLR, 2022.","title":"Compressed-vfl: Communication-efficient learning with vertically partitioned data","authors":"Timothy J Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson","year":"2022","venue":"International Conference on Machine Learning, ICML 2022","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chandar et al., 2021] Srikanth Chandar, Pravin Chandran, Raghavendra Bhat, and Avinash Chakravarthi. Commu- nication optimization in large scale federated learning using autoencoder compressed weight updates. CoRR, abs/2108.05670, 2021.","title":"Communication optimization in large scale federated learning using autoencoder compressed weight updates","authors":"Srikanth Chandar, Pravin Chandran, Raghavendra Bhat, Avinash Chakravarthi","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2021a] Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, and Cheng Hong. When homomorphic en- cryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control. In Feida Zhu, Beng Chin Ooi, and Chunyan Miao, editors, KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discov- ery and Data Mining, pages 2652-2662. ACM, 2021.","title":"When homomorphic encryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control","authors":"Chen","year":"2021","venue":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2021b] Xing Chen, Jingtao Li, and Chaitali Chakrabarti. Communication and computation reduction for split learning using asynchronous training. In IEEE Workshop on Signal Processing Systems, SiPS 2021, pages 76-81. IEEE, 2021.","title":"Communication and computation reduction for split learning using asynchronous training","authors":"Chen","year":"2021","venue":"IEEE Workshop on Signal Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2022] Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, and Xiaolin Zheng. Vertically fed- erated graph neural network for privacy-preserving node classification. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pages 1959-1965. ijcai.org, 2022.","title":"Vertically federated graph neural network for privacy-preserving node classification","authors":"Chen","year":"2022","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022","doi":null,"url":null,"internal_url":"/proceedings/2022/272"},{"position":10,"raw":"[Fu et al., 2022] Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, and Ting Wang. Label inference attacks against vertical federated learning. In 31st USENIX Se- curity Symposium (USENIX Security 22), Boston, MA, August 2022. USENIX Association.","title":"Label inference attacks against vertical federated learning","authors":"Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X Liu, Ting Wang","year":"2022","venue":"31st USENIX Security Symposium (USENIX Security 22)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gholami et al., 2021] Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, and Kurt Keutzer. A survey of quantization methods for efficient neural net- work inference. CoRR, abs/2103.13630, 2021.","title":"A survey of quantization methods for efficient neural network inference","authors":"Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, Kurt Keutzer","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gouk et al., 2021] Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J. Cree. Regularisation of neural networks by enforcing lipschitz continuity. Mach. Learn., 110(2):393-416, 2021.","title":"Regularisation of neural networks by enforcing lipschitz continuity","authors":"Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J Cree","year":"2021","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pages 770-778. IEEE Computer Society, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hidasi et al., 2016] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based rec- ommendations with recurrent neural networks. In 4th Inter- national Conference on Learning Representations, ICLR 2016, 2016.","title":"Session-based recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2016","venue":"4th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jacob et al., 2018] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In 2018 IEEE Confer- ence on Computer Vision and Pattern Recognition, CVPR 2018, pages 2704-2713. Computer Vision Foundation / IEEE Computer Society, 2018.","title":"Quantization and training of neural networks for efficient integer-arithmetic-only inference","authors":"Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G Howard, Hartwig Adam, Dmitry Kalenichenko","year":"2018","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jin et al., 2022] Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, and Dejing Dou. Accelerated feder- ated learning with decoupled adaptive optimization. In In- ternational Conference on Machine Learning, pages 10298- 10322. PMLR, 2022.","title":"Accelerated federated learning with decoupled adaptive optimization","authors":"Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Karimireddy et al., 2020] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. SCAFFOLD: stochastic controlled averaging for federated learning. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 5132- 5143. PMLR, 2020.","title":"SCAFFOLD: stochastic controlled averaging for federated learning","authors":"Praneeth Sai, Satyen Karimireddy, Mehryar Kale, Mohri, J Sashank, Sebastian U Reddi, Ananda Stich, Suresh Theertha","year":"2020","venue":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kim, 2014] Yoon Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Confer- ence on Empirical Methods in Natural Language Process- ing, EMNLP 2014, pages 1746-1751. ACL, 2014.","title":"Convolutional neural networks for sentence classification","authors":"Yoon Kim","year":"2014","venue":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Krizhevsky et al., 2009] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny im- ages. Master's thesis, Department of Computer Science, University of Toronto, 2009.","title":"Learning multiple layers of features from tiny images","authors":"Alex Krizhevsky, Geoffrey Hinton","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Le and Yang, 2015] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015.","title":"Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge","authors":"Yang Le","year":"2015","venue":"CS","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2020] Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, meth- ods, and future directions. IEEE Signal Processing Maga- zine, 37(3):50-60, 2020.","title":"Federated learning: Challenges, methods, and future directions","authors":"Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith","year":"2020","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[McMahan et al., 2017] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Ar- cas. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th Inter- national Conference on Artificial Intelligence and Statis- tics, AISTATS 2017, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282. PMLR, 2017.","title":"Communication-efficient learning of deep networks from decentralized data","authors":"Brendan Mcmahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera Y Arcas","year":"2017","venue":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Mohassel and Rindal, 2018] Payman Mohassel and Peter Rindal. Aby 3 : A mixed protocol framework for machine learning. In Proceedings of the 2018 ACM SIGSAC Con- ference on Computer and Communications Security, CCS 2018, pages 35-52. ACM, 2018.","title":"Aby 3 : A mixed protocol framework for machine learning","authors":"Rindal Mohassel, Payman Mohassel, Peter Rindal","year":"2018","venue":"Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Neyshabur et al., 2015] Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro. Norm-based capacity control in neural networks. In Proceedings of The 28th Conference on Learning Theory, COLT 2015, volume 40 of JMLR Workshop and Conference Proceedings, pages 1376-1401.","title":"Norm-based capacity control in neural networks","authors":"Ryota Behnam Neyshabur, Nathan Tomioka, Srebro","year":null,"venue":"Proceedings of The 28th Conference on Learning Theory, COLT 2015","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"JMLR.org, 2015.","title":"JMLR","authors":null,"year":"2015","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Neyshabur et al., 2017] Behnam Neyshabur, Srinadh Bho- janapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. In Advances in Neural Information Processing Systems 30: NeurIPS 2017, pages 5947-5956, 2017.","title":"Exploring generalization in deep learning","authors":"Srinadh Behnam Neyshabur, David Bhojanapalli, Nati Mcallester, Srebro","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 confer- ence on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher D Manning","year":"2014","venue":"Proceedings of the 2014 conference on empirical methods in natural language processing","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Sattler et al., 2019a] Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. Robust and communication-efficient federated learning from non-iid data. CoRR, abs/1903.02891, 2019.","title":"Robust and communication-efficient federated learning from non-iid data","authors":"Sattler","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sattler et al., 2019b] Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. Sparse bi- nary compression: Towards distributed deep learning with minimal communication. In International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14-19, 2019, pages 1-8. IEEE, 2019.","title":"Sparse binary compression: Towards distributed deep learning with minimal communication","authors":"Sattler","year":"2019","venue":"International Joint Conference on Neural Networks, IJCNN 2019","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Stich et al., 2018] Sebastian U. Stich, Jean-Baptiste Cordon- nier, and Martin Jaggi. Sparsified SGD with memory. In Advances in Neural Information Processing Systems 31: NeurIPS 2018, pages 4452-4463, 2018.","title":"Sparsified SGD with memory","authors":"Sebastian U Stich, Jean-Baptiste Cordonnier, Martin Jaggi","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tan and Le, 2019] Mingxing Tan and Quoc V. Le. Efficient- net: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Con- ference on Machine Learning, ICML 2019, volume 97 of Proceedings of Machine Learning Research, pages 6105- 6114. PMLR, 2019.","title":"Efficientnet: Rethinking model scaling for convolutional neural networks","authors":"Mingxing Tan, V Quoc, Le","year":"2019","venue":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tibshirani, 1996] Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267-288, 1996.","title":"Regression shrinkage and selection via the lasso","authors":"Robert Tibshirani","year":"1996","venue":"Journal of the Royal Statistical Society: Series B (Methodological)","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Vepakomma et al., 2018] Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. Split learning for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564, 2018.","title":"Split learning for health: Distributed deep learning without sharing raw patient data","authors":"Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Vogels et al., 2019] Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi. Powersgd: Practical low-rank gradient compression for distributed optimization. In Advances in Neural Information Processing Systems 32: NeurIPS 2019, pages 14236-14245, 2019.","title":"Powersgd: Practical low-rank gradient compression for distributed optimization","authors":"Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wagh et al., 2019] Sameer Wagh, Divya Gupta, and Nis- hanth Chandran. Securenn: 3-party secure computation for neural network training. Proc. Priv. Enhancing Technol., 2019(3):26-49, 2019.","title":"Securenn: 3-party secure computation for neural network training","authors":"Sameer Wagh, Divya Gupta, Nishanth Chandran","year":"2019","venue":"Proc. Priv. Enhancing Technol","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2018] Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary B. Charles, Dimitris S. Papailiopoulos, and Stephen J. Wright. ATOMO: communication-efficient learning via atomic sparsification. In Advances in Neu- ral Information Processing Systems 31: NeurIPS 2018, pages 9872-9883, 2018.","title":"ATOMO: communication-efficient learning via atomic sparsification","authors":"Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary B Charles, Dimitris S Papailiopoulos, Stephen J Wright","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2020a] Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, and Yasaman Khaz- aeni. Federated learning with matched averaging. In 8th In- ternational Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenRe- view.net, 2020.","title":"Papailiopoulos, and Yasaman Khazaeni. Federated learning with matched averaging","authors":"Wang","year":"2020","venue":"8th International Conference on Learning Representations, ICLR 2020","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2020b] Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. Tackling the objec- tive inconsistency problem in heterogeneous federated opti- mization. In Advances in Neural Information Processing Systems 33: NeurIPS 2020, 2020.","title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","authors":"Wang","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wen et al., 2017] Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. Terngrad: Ternary gradients to reduce communication in distributed deep learning. In Advances in Neural Information Process- ing Systems 30: NeurIPS 2017, pages 1509-1519, 2017.","title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning","authors":"Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li","year":"2017","venue":"Advances in Neural Information Processing Systems 30: NeurIPS 2017","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wright et al., 2008] John Wright, Allen Y Yang, Arvind Ganesh, S Shankar Sastry, and Yi Ma. Robust face recogni- tion via sparse representation. IEEE transactions on pattern analysis and machine intelligence, 31(2):210-227, 2008.","title":"Robust face recognition via sparse representation","authors":"John Wright, Allen Y Yang, Arvind Ganesh, S Shankar Sastry, Yi Ma","year":"2008","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yin et al., 2012] Jun Yin, Zhonghua Liu, Zhong Jin, and Wankou Yang. Kernel sparse representation based classifi- cation. Neurocomputing, 77(1):120-128, 2012.","title":"Kernel sparse representation based classification","authors":"Yin","year":"2012","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhu and Han, 2020] Ligeng Zhu and Song Han. Deep leak- age from gradients. In Federated Learning -Privacy and Incentive, volume 12500 of Lecture Notes in Computer Science, pages 17-31. Springer, 2020.","title":"Deep leakage from gradients","authors":"Han ; Zhu, Ligeng Zhu, Song Han","year":"2020","venue":"Federated Learning -Privacy and Incentive","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}