{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:32:38Z","timestamp":1771705958208,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,12]]},"DOI":"10.1145\/3673038.3673142","type":"proceedings-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T18:29:01Z","timestamp":1723141741000},"page":"866-875","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0749-2060","authenticated-orcid":false,"given":"Zichen","family":"Tang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6544-9720","authenticated-orcid":false,"given":"Junlin","family":"Huang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8960-7904","authenticated-orcid":false,"given":"Rudan","family":"Yan","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-1419","authenticated-orcid":false,"given":"Yuxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-9974","authenticated-orcid":false,"given":"Zhenheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1418-5160","authenticated-orcid":false,"given":"Shaohuai","family":"Shi","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9773-9332","authenticated-orcid":false,"given":"Amelie Chi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9745-4372","authenticated-orcid":false,"given":"Xiaowen","family":"Chu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Federated sparse training: Lottery aware model compression for resource constrained edge. arXiv preprint arXiv:2208.13092","author":"Babakniya Sara","year":"2022","unstructured":"Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, and Salman Avestimehr. 2022. Federated sparse training: Lottery aware model compression for resource constrained edge. arXiv preprint arXiv:2208.13092 (2022)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20555"},{"key":"e_1_3_2_1_3_1","volume-title":"Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas, Peter Wu, Tian Li, Jakub Kone\u010dn\u1ef3, H\u00a0Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"Communication-efficient federated learning with adaptive parameter freezing. In 2021 IEEE 41st ICDCS","author":"Chen Chen","unstructured":"Chen Chen, Hong Xu, Wei Wang, Baochun Li, Bo Li, Li Chen, and Gong Zhang. 2021. Communication-efficient federated learning with adaptive parameter freezing. In 2021 IEEE 41st ICDCS. IEEE, 1\u201311."},{"key":"e_1_3_2_1_5_1","volume-title":"Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876","author":"Chen Fei","year":"2018","unstructured":"Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876 (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"ICML. PMLR","author":"Collins Liam","year":"2021","unstructured":"Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021. Exploiting shared representations for personalized federated learning. In ICML. PMLR, 2089\u20132099."},{"key":"e_1_3_2_1_7_1","volume-title":"Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models. arXiv preprint arXiv:2311.04902","author":"Das Rocktim\u00a0Jyoti","year":"2023","unstructured":"Rocktim\u00a0Jyoti Das, Liqun Ma, and Zhiqiang Shen. 2023. Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models. arXiv preprint arXiv:2311.04902 (2023)."},{"key":"e_1_3_2_1_8_1","unstructured":"Peijie Dong Lujun Li Zhenheng Tang Xiang Liu Xinglin Pan Qiang Wang and Xiaowen Chu. 2024. Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models. In ICML."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Peijie Dong Lujun Li and Zimian Wei. 2023. DisWOT: Student Architecture Search for Distillation WithOut Training. In CVPR. 11898\u201311908.","DOI":"10.1109\/CVPR52729.2023.01145"},{"key":"e_1_3_2_1_10_1","volume-title":"EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization. In ICCV. 17076\u201317086.","author":"Dong Peijie","year":"2023","unstructured":"Peijie Dong, Lujun Li, Zimian Wei, Xin Niu, Zhiliang Tian, and Hengyue Pan. 2023. EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization. In ICCV. 17076\u201317086."},{"key":"e_1_3_2_1_11_1","volume-title":"Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948","author":"Fallah Alireza","year":"2020","unstructured":"Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 (2020)."},{"key":"e_1_3_2_1_12_1","volume-title":"Narrowband IoT: A Survey on Downlink and Uplink Perspectives","author":"Feltrin Luca","year":"2019","unstructured":"Luca Feltrin, Galini Tsoukaneri, and others.2019. Narrowband IoT: A Survey on Downlink and Uplink Perspectives. IEEE Wireless Communications 26 (02 2019), 78\u201386."},{"key":"e_1_3_2_1_13_1","volume-title":"Quantization robust federated learning for efficient inference on heterogeneous devices. arXiv preprint arXiv:2206.10844","author":"Gupta Kartik","year":"2022","unstructured":"Kartik Gupta, Marios Fournarakis, Matthias Reisser, Christos Louizos, and Markus Nagel. 2022. Quantization robust federated learning for efficient inference on heterogeneous devices. arXiv preprint arXiv:2206.10844 (2022)."},{"key":"e_1_3_2_1_14_1","volume-title":"Federated Learning with Compression: Unified Analysis and Sharp Guarantees. arXiv preprint arXiv:2007.01154","author":"Haddadpour Farzin","year":"2020","unstructured":"Farzin Haddadpour, Mohammad\u00a0Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi. 2020. Federated Learning with Compression: Unified Analysis and Sharp Guarantees. arXiv preprint arXiv:2007.01154 (2020)."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2350\u20132358","author":"Haddadpour Farzin","year":"2021","unstructured":"Farzin Haddadpour, Mohammad\u00a0Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi. 2021. Federated learning with compression: Unified analysis and sharp guarantees. In International Conference on Artificial Intelligence and Statistics. PMLR, 2350\u20132358."},{"key":"e_1_3_2_1_16_1","volume-title":"Adaptive gradient sparsification for efficient federated learning: An online learning approach. In 2020 IEEE 40th ICDCS","author":"Han Pengchao","unstructured":"Pengchao Han, Shiqiang Wang, and Kin\u00a0K Leung. 2020. Adaptive gradient sparsification for efficient federated learning: An online learning approach. In 2020 IEEE 40th ICDCS. IEEE, 300\u2013310."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_18_1","unstructured":"Peng Jiang and Gagan Agrawal. 2018. A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. In Advances in Neural Information Processing Systems. 2530\u20132541."},{"key":"e_1_3_2_1_19_1","volume-title":"Model pruning enables efficient federated learning on edge devices","author":"Jiang Yuang","year":"2022","unstructured":"Yuang Jiang, Shiqiang Wang, Victor Valls, Bong\u00a0Jun Ko, Wei-Han Lee, Kin\u00a0K Leung, and Leandros Tassiulas. 2022. Model pruning enables efficient federated learning on edge devices. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_3_2_1_20_1","unstructured":"Peter Kairouz H.\u00a0Brendan McMahan Brendan Avent and et al.2021. Advances and Open Problems in Federated Learning. arxiv:1912.04977\u00a0[cs.LG]"},{"key":"e_1_3_2_1_21_1","volume-title":"Scaffold: Stochastic controlled averaging for federated learning. In ICML. PMLR, 5132\u20135143.","author":"Karimireddy Sai\u00a0Praneeth","year":"2020","unstructured":"Sai\u00a0Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda\u00a0Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In ICML. PMLR, 5132\u20135143."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_1_23_1","volume-title":"Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492","author":"Kone\u010dn\u1ef3 Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u1ef3, H\u00a0Brendan McMahan, Felix\u00a0X Yu, Peter Richt\u00e1rik, Ananda\u00a0Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)."},{"key":"e_1_3_2_1_24_1","unstructured":"Qinbin Li Yiqun Diao Quan Chen and Bingsheng He. 2021. Federated Learning on Non-IID Data Silos: An Experimental Study. arxiv:2102.02079\u00a0[cs.LG]"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_2_1_26_1","volume-title":"Ditto: Fair and robust federated learning through personalization. In ICML. PMLR, 6357\u20136368.","author":"Li Tian","year":"2021","unstructured":"Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021. Ditto: Fair and robust federated learning through personalization. In ICML. PMLR, 6357\u20136368."},{"key":"e_1_3_2_1_27_1","volume-title":"Federated Recommendation System via Differential Privacy. arXiv preprint arXiv:2005.06670","author":"Li Tan","year":"2020","unstructured":"Tan Li, Linqi Song, and Christina Fragouli. 2020. Federated Recommendation System via Differential Privacy. arXiv preprint arXiv:2005.06670 (2020)."},{"key":"e_1_3_2_1_28_1","volume-title":"On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019)."},{"key":"e_1_3_2_1_29_1","volume-title":"PMLR","author":"Li Xiaoyun","year":"2023","unstructured":"Xiaoyun Li and Ping Li. 2023. Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. In ICML, Vol.\u00a0202. PMLR, 19638\u201319688."},{"key":"e_1_3_2_1_30_1","volume-title":"Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523","author":"Liang Paul\u00a0Pu","year":"2020","unstructured":"Paul\u00a0Pu Liang, Terrance Liu, Liu Ziyin, Nicholas\u00a0B Allen, Randy\u00a0P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)."},{"key":"e_1_3_2_1_31_1","volume-title":"Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors 21, 12","author":"Loh Frank","year":"2021","unstructured":"Frank Loh, Fabian Poign\u00e9e, Florian Wamser, Ferdinand Leidinger, and Tobias Ho\u00dffeld. 2021. Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors 21, 12 (2021)."},{"key":"e_1_3_2_1_32_1","unstructured":"Mi Luo Fei Chen Dapeng Hu Yifan Zhang Jian Liang and Jiashi Feng. 2021. No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. In Advances in Neural Information Processing Systems A.\u00a0Beygelzimer Y.\u00a0Dauphin P.\u00a0Liang and J.\u00a0Wortman Vaughan (Eds.)."},{"key":"e_1_3_2_1_33_1","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume":"34","author":"Marfoq Othmane","year":"2021","unstructured":"Othmane Marfoq, Giovanni Neglia, Aur\u00e9lien Bellet, Laetitia Kameni, and Richard Vidal. 2021. Federated multi-task learning under a mixture of distributions. Advances in Neural Information Processing Systems 34 (2021), 15434\u201315447.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. 1273\u20131282."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01152"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Ngoc-Hieu Nguyen Tuan-Anh Nguyen Tuan Nguyen Vu\u00a0Tien Hoang Dung\u00a0D Le and Kok-Seng Wong. 2024. Towards Efficient Communication Federated Recommendation System via Low-rank Training. In arXiv:2401.03748.","DOI":"10.1145\/3589334.3645702"},{"key":"e_1_3_2_1_37_1","volume-title":"Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. arXiv preprint arXiv:2006.14591","author":"Philippenko Constantin","year":"2020","unstructured":"Constantin Philippenko and Aymeric Dieuleveut. 2020. Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. arXiv preprint arXiv:2006.14591 (2020)."},{"key":"e_1_3_2_1_38_1","unstructured":"Xinchi Qiu Javier Fernandez-Marques Pedro\u00a0PB Gusmao Yan Gao Titouan Parcollet and Nicholas\u00a0Donald Lane. 2022. ZeroFL: Efficient on-device training for federated learning with local sparsity. arXiv preprint arXiv:2208.02507 (2022)."},{"key":"e_1_3_2_1_39_1","volume-title":"Adaptive Federated Optimization. arXiv preprint arXiv:2003.00295","author":"Reddi Sashank","year":"2020","unstructured":"Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dn\u1ef3, Sanjiv Kumar, and H\u00a0Brendan McMahan. 2020. Adaptive Federated Optimization. arXiv preprint arXiv:2003.00295 (2020)."},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR","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 International Conference on Artificial Intelligence and Statistics. PMLR, 2021\u20132031."},{"key":"e_1_3_2_1_41_1","volume-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE transactions on neural networks and learning systems 31, 9 (2019), 3400\u20133413."},{"key":"e_1_3_2_1_42_1","volume-title":"Model compression for communication efficient federated learning","author":"Shah Suhail\u00a0Mohmad","year":"2021","unstructured":"Suhail\u00a0Mohmad Shah and Vincent\u00a0KN Lau. 2021. Model compression for communication efficient federated learning. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000530"},{"key":"e_1_3_2_1_44_1","volume-title":"A distributed synchronous SGD algorithm with global top-k sparsification for low bandwidth networks. In 2019 IEEE 39th ICDCS","author":"Shi Shaohuai","unstructured":"Shaohuai Shi, Qiang Wang, Kaiyong Zhao, Zhenheng Tang, Yuxin Wang, Xiang Huang, and Xiaowen Chu. 2019. A distributed synchronous SGD algorithm with global top-k sparsification for low bandwidth networks. In 2019 IEEE 39th ICDCS. IEEE, 2238\u20132247."},{"key":"e_1_3_2_1_45_1","first-page":"401","article-title":"Towards scalable distributed training of deep learning on public cloud clusters","volume":"3","author":"Shi Shaohuai","year":"2021","unstructured":"Shaohuai Shi, Xianhao Zhou, Shutao Song, Xingyao Wang, Zilin Zhu, Xue Huang, Xinan Jiang, Feihu Zhou, Zhenyu Guo, Liqiang Xie, 2021. Towards scalable distributed training of deep learning on public cloud clusters. MLSys 3, 401\u2013412.","journal-title":"MLSys"},{"key":"e_1_3_2_1_46_1","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"T\u00a0Dinh Canh","year":"2020","unstructured":"Canh T\u00a0Dinh, Nguyen Tran, and Josh Nguyen. 2020. Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems 33 (2020), 21394\u201321405.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_47_1","volume-title":"d.]. Communication-efficient decentralized learning with sparsification and adaptive peer selection. In 2020 IEEE 40th ICDCS","author":"Tang Zhenheng","unstructured":"Zhenheng Tang, Shaohuai Shi, and Xiaowen Chu. [n. d.]. Communication-efficient decentralized learning with sparsification and adaptive peer selection. In 2020 IEEE 40th ICDCS. IEEE, 1207\u20131208."},{"key":"e_1_3_2_1_48_1","volume-title":"Communication-efficient distributed deep learning: A comprehensive survey. arXiv preprint arXiv:2003.06307","author":"Tang Zhenheng","year":"2020","unstructured":"Zhenheng Tang, Shaohuai Shi, Xiaowen Chu, Wei Wang, and Bo Li. 2020. Communication-efficient distributed deep learning: A comprehensive survey. arXiv preprint arXiv:2003.06307 (2020)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","unstructured":"Zhenheng Tang Shaohuai Shi Bo Li and Xiaowen Chu. 2022. GossipFL: A Decentralized Federated Learning Framework with Sparsified and Adaptive Communication. IEEE TPDS (2022) 1\u201313. https:\/\/doi.org\/10.1109\/TPDS.2022.3230938","DOI":"10.1109\/TPDS.2022.3230938"},{"key":"e_1_3_2_1_50_1","volume-title":"The 32nd International Joint Conference on Artificial Intelligence, Symposium on Large Language Models.","author":"Tang Zhenheng","year":"2023","unstructured":"Zhenheng Tang, Yuxin Wang, Xin He, Longteng Zhang, Xinglin Pan, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Bingsheng He, 2023. Fusionai: Decentralized training and deploying llms with massive consumer-level gpus. In The 32nd International Joint Conference on Artificial Intelligence, Symposium on Large Language Models."},{"key":"e_1_3_2_1_51_1","unstructured":"Zhenheng Tang Yonggang Zhang Shaohuai Shi Xin He Bo Han and Xiaowen Chu. 2022. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. In ICML Vol.\u00a0162."},{"key":"e_1_3_2_1_52_1","unstructured":"Zhenheng Tang Yonggang Zhang Shaohuai Shi Xinmei Tian Tongliang Liu Bo Han and Xiaowen Chu. 2024. FedImpro: Measuring and Improving Client Update in Federated Learning. In ICLR."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342005051521"},{"key":"e_1_3_2_1_54_1","unstructured":"Thijs Vogels. 2023. Communication-efficient distributed training of machine learning models. Technical Report. EPFL."},{"key":"e_1_3_2_1_55_1","volume-title":"Optimizing Federated Learning on Non-IID Data with Reinforcement Learning","author":"Wang Hao","unstructured":"Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li. 2020. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning. In IEEE INFOCOM. 1698\u20131707."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Yuxin Wang Yuhan Chen Zeyu Li Xueze Kang Zhenheng Tang Xin He Rui Guo Xin Wang Qiang Wang Amelie\u00a0Chi Zhou and Xiaowen Chu. 2024. BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems.","DOI":"10.1145\/3711896.3737413"},{"key":"e_1_3_2_1_57_1","unstructured":"Yujia Wang Lu Lin and Jinghui Chen. 2022. Communication-efficient adaptive federated learning. In ICML. PMLR 22802\u201322838."},{"key":"e_1_3_2_1_58_1","volume-title":"Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining. ArXiv","author":"Wang Yuxin","year":"2023","unstructured":"Yuxin Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Xinglin Pan, Yang Zheng, Xiaoyu Wu, Amelie\u00a0Chi Zhou, Bingsheng He, and Xiaowen Chu. 2023. Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining. ArXiv (2023)."},{"key":"e_1_3_2_1_59_1","unstructured":"Jianqiao Wangni Jialei Wang Ji Liu and Tong Zhang. 2018. Gradient sparsification for communication-efficient distributed optimization. In NeurIPS. 1299\u20131309."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26235"}],"event":{"name":"ICPP '24: the 53rd International Conference on Parallel Processing","location":"Gotland Sweden","acronym":"ICPP '24"},"container-title":["Proceedings of the 53rd International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3673038.3673142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:28:01Z","timestamp":1758648481000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":60,"alternative-id":["10.1145\/3673038.3673142","10.1145\/3673038"],"URL":"https:\/\/doi.org\/10.1145\/3673038.3673142","relation":{},"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"2024-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}