{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T05:54:04Z","timestamp":1770357244696,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Science and Technology Support Program of Hubei Province","award":["2022BAA046"],"award-info":[{"award-number":["2022BAA046"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376103, 62302184, 62206102"],"award-info":[{"award-number":["62376103, 62302184, 62206102"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ant Group through CCF-Ant Research Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680608","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"3686-3694","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Masked Random Noise for Communication-Efficient Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-0275","authenticated-orcid":false,"given":"Shiwei","family":"Li","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9766-4024","authenticated-orcid":false,"given":"Yingyi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7591-5315","authenticated-orcid":false,"given":"Haozhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4360-0754","authenticated-orcid":false,"given":"Xing","family":"Tang","sequence":"additional","affiliation":[{"name":"FiT, Tencent, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6279-190X","authenticated-orcid":false,"given":"Shijie","family":"Xu","sequence":"additional","affiliation":[{"name":"FiT, Tencent, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4329-4923","authenticated-orcid":false,"given":"Weihong","family":"Luo","sequence":"additional","affiliation":[{"name":"FiT, Tencent, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1846-4941","authenticated-orcid":false,"given":"Yuhua","family":"Li","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3612-709X","authenticated-orcid":false,"given":"Dugang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4115-8205","authenticated-orcid":false,"given":"Xiuqiang","family":"He","sequence":"additional","affiliation":[{"name":"FiT, Tencent, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7791-5511","authenticated-orcid":false,"given":"Ruixuan","family":"Li","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research","volume":"174","author":"Aladago Maxwell Mbabilla","year":"2021","unstructured":"Maxwell Mbabilla Aladago and Lorenzo Torresani. 2021. Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks. In Proceedings of the 38th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research, Vol. 139). PMLR, 163--174."},{"key":"e_1_3_2_1_3_1","unstructured":"Jeremy Bernstein et al. 2018. signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant. CoRR (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics, COMPSTAT. Physica-Verlag, 177--186","author":"Bottou L\u00e9on","year":"2010","unstructured":"L\u00e9on Bottou. 2010. Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics, COMPSTAT. Physica-Verlag, 177--186."},{"key":"e_1_3_2_1_5_1","volume-title":"LEAF: A Benchmark for Federated Settings. CoRR","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas, Peter Wu, Tian Li, Jakub Konevcn\u00fd, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. LEAF: A Benchmark for Federated Settings. CoRR, Vol. abs\/1812.01097 (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"Trainable Neural Networks. In 7th International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Frankle Jonathan","year":"2019","unstructured":"Jonathan Frankle and Michael Carbin. 2019. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. In 7th International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS. 315--323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS. 315--323."},{"key":"e_1_3_2_1_8_1","volume-title":"Neural networks for machine learning. Coursera video lectures","author":"Hinton Geoffrey","year":"2012","unstructured":"Geoffrey Hinton. 2012. Neural networks for machine learning. Coursera video lectures (2012)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning, ICML","volume":"162","author":"H\u00f6nig Robert","unstructured":"Robert H\u00f6nig, Yiren Zhao, and Robert D. Mullins. 2022. DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. In International Conference on Machine Learning, ICML, Vol. 162. PMLR, 8852--8866."},{"key":"e_1_3_2_1_11_1","volume-title":"FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Hyeon-Woo Nam","year":"2022","unstructured":"Nam Hyeon-Woo, Moon Ye-Bin, and Tae-Hyun Oh. 2022. FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, ICML. 448--456","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, ICML. 448--456."},{"key":"e_1_3_2_1_13_1","volume-title":"Sparse Random Networks for Communication-Efficient Federated Learning. In The Eleventh International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Isik Berivan","year":"2023","unstructured":"Berivan Isik, Francesco Pase, Deniz G\u00fcnd\u00fcz, Tsachy Weissman, and Michele Zorzi. 2023. Sparse Random Networks for Communication-Efficient Federated Learning. In The Eleventh International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_14_1","volume-title":"Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees. CoRR","author":"Jin Richeng","year":"2020","unstructured":"Richeng Jin, Yufan Huang, Xiaofan He, Huaiyu Dai, and Tianfu Wu. 2020. Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees. CoRR, Vol. abs\/2002.10940 (2020)."},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning,ICML","volume":"97","author":"Karimireddy Sai Praneeth","year":"2019","unstructured":"Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, and Martin Jaggi. 2019. Error Feedback Fixes SignSGD and other Gradient Compression Schemes. In Proceedings of the 36th International Conference on Machine Learning,ICML, Vol. 97. PMLR, 3252--3261."},{"key":"e_1_3_2_1_16_1","volume-title":"Invert. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Koster Nils","year":"2022","unstructured":"Nils Koster, Oliver Grothe, and Achim Rettinger. 2022. Signing the Supermask: Keep, Hide, Invert. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_17_1","volume-title":"Learning multiple layers of features from tiny images. Technical report","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"e_1_3_2_1_19_1","volume-title":"Federated Learning on Non-IID Data Silos: An Experimental Study. In 38th IEEE International Conference on Data Engineering, ICDE. IEEE, 965--978","author":"Li Qinbin","year":"2022","unstructured":"Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He. 2022. Federated Learning on Non-IID Data Silos: An Experimental Study. In 38th IEEE International Conference on Data Engineering, ICDE. IEEE, 965--978."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning, ICML. PMLR.","author":"Li Shiwei","year":"2024","unstructured":"Shiwei Li, Wenchao Xu, Haozhao Wang, Xing Tang, Yining Qi, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, and Ruixuan Li. 2024. FedBAT: Communication-Efficient Federated Learning via Learnable Binarization. In Proceedings of the 41st International Conference on Machine Learning, ICML. PMLR."},{"key":"e_1_3_2_1_21_1","volume-title":"On the Convergence of FedAvg on Non-IID Data. In 8th International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Li Xiang","year":"2020","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020. On the Convergence of FedAvg on Non-IID Data. In 8th International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_22_1","volume-title":"Bridging Discrete and Backpropagation: Straight-Through and Beyond. CoRR","author":"Liu Liyuan","year":"2023","unstructured":"Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu, and Jianfeng Gao. 2023. Bridging Discrete and Backpropagation: Straight-Through and Beyond. CoRR, Vol. abs\/2304.08612 (2023)."},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research","volume":"6691","author":"Malach Eran","year":"2020","unstructured":"Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, and Ohad Shamir. 2020. Proving the Lottery Ticket Hypothesis: Pruning is All You Need. In Proceedings of the 37th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 6682--6691."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS","volume":"54","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS, Vol. 54. PMLR, 1273--1282."},{"key":"e_1_3_2_1_25_1","volume-title":"FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023","author":"Miao Jiaxu","year":"2023","unstructured":"Jiaxu Miao, Zongxin Yang, Leilei Fan, and Yi Yang. 2023. FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17--24, 2023. IEEE, 8042--8052."},{"key":"e_1_3_2_1_26_1","volume-title":"NIPS Workshop on Deep Learning and Unsupervised Feature Learning.","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning."},{"key":"e_1_3_2_1_27_1","volume-title":"Papailiopoulos","author":"Pensia Ankit","year":"2020","unstructured":"Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris S. Papailiopoulos. 2020. Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient. CoRR, Vol. abs\/2006.07990 (2020)."},{"key":"e_1_3_2_1_28_1","volume-title":"ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net.","author":"Qiu Xinchi","year":"2022","unstructured":"Xinchi Qiu, Javier Fern\u00e1ndez-Marqu\u00e9s, Pedro P. B. de Gusmao, Yan Gao, Titouan Parcollet, and Nicholas Donald Lane. 2022. ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. In The Tenth International Conference on Learning Representations, ICLR. OpenReview.net."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01191"},{"key":"e_1_3_2_1_30_1","volume-title":"FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS","volume":"108","author":"Reisizadeh Amirhossein","year":"2020","unstructured":"Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani. 2020. FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS, Vol. 108. PMLR, 2021--2031."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML","volume":"139","author":"Safaryan Mher","year":"2021","unstructured":"Mher Safaryan and Peter Richt\u00e1rik. 2021. Stochastic Sign Descent Methods: New Algorithms and Better Theory. In Proceedings of the 38th International Conference on Machine Learning, ICML, Vol. 139. PMLR, 9224--9234."},{"key":"e_1_3_2_1_32_1","volume-title":"Sparsified SGD with Memory. In Advances in Neural Information Processing Systems 31: Annual Conferenceon Neural Information Processing Systems, NeurIPS. 4452--4463","author":"Stich Sebastian U.","year":"2018","unstructured":"Sebastian U. Stich, Jean-Baptiste Cordonnier, and Martin Jaggi. 2018. Sparsified SGD with Memory. In Advances in Neural Information Processing Systems 31: Annual Conferenceon Neural Information Processing Systems, NeurIPS. 4452--4463."},{"key":"e_1_3_2_1_33_1","volume-title":"Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS","author":"Stripelis Dimitris","year":"2022","unstructured":"Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, and Jose Luis Ambite. 2022. Federated Progressive Sparsification (Purge-Merge-Tune). In Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)."},{"key":"e_1_3_2_1_34_1","volume-title":"z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning. CoRR","author":"Tang Zhiwei","year":"2023","unstructured":"Zhiwei Tang, Yanmeng Wang, and Tsung-Hui Chang. 2023. z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning. CoRR, Vol. abs\/2302.02589 (2023)."},{"key":"e_1_3_2_1_35_1","volume-title":"Hengwei Xu, and Pan Hui.","author":"Vallapuram Anish K.","year":"2022","unstructured":"Anish K. Vallapuram, Pengyuan Zhou, Young D. Kwon, Lik Hang Lee, Hengwei Xu, and Pan Hui. 2022. HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask. CoRR, Vol. abs\/2206.04385 (2022)."},{"key":"e_1_3_2_1_36_1","volume-title":"EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. In International Conference on Machine Learning, ICML","volume":"162","author":"Vargaftik Shay","year":"2022","unstructured":"Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, and Michael Mitzenmacher. 2022. EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. In International Conference on Machine Learning, ICML, Vol. 162. PMLR, 21984--22014."},{"key":"e_1_3_2_1_37_1","volume-title":"DRIVE: One-bit Distributed Mean Estimation. In Advances in Neural Information Processing Systems, NeurIPS. 362--377.","author":"Vargaftik Shay","year":"2021","unstructured":"Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, and Michael Mitzenmacher. 2021. DRIVE: One-bit Distributed Mean Estimation. In Advances in Neural Information Processing Systems, NeurIPS. 362--377."},{"key":"e_1_3_2_1_38_1","unstructured":"Wei Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2017. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning. In Advances in Neural Information Processing Systems NIPS. 1509--1519."},{"key":"e_1_3_2_1_39_1","volume-title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR","author":"Xiao Han","year":"2017","unstructured":"Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR, Vol. abs\/1708.07747 (2017)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01515-2"},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML","volume":"97","author":"Yurochkin Mikhail","year":"2019","unstructured":"Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian Nonparametric Federated Learning of Neural Networks. In Proceedings of the 36th International Conference on Machine Learning, ICML, Vol. 97. PMLR, 7252--7261."},{"key":"e_1_3_2_1_42_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, NeurIPS. 3592--3602","author":"Zhou Hattie","year":"2019","unstructured":"Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski. 2019. Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, NeurIPS. 3592--3602."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680608","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:56Z","timestamp":1750295876000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":42,"alternative-id":["10.1145\/3664647.3680608","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680608","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}