{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:04:58Z","timestamp":1764777898667,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["#2321123","#2333324","#2340982","#2505106"],"award-info":[{"award-number":["#2321123","#2333324","#2340982","#2505106"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"DOE U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0024207","DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-SC0024207","DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,3]]},"DOI":"10.1145\/3769102.3770613","type":"proceedings-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:00:41Z","timestamp":1764777641000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1522-7686","authenticated-orcid":false,"given":"Zhonghao","family":"Chen","sequence":"first","affiliation":[{"name":"University of California, Merced, Merced, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0573-8808","authenticated-orcid":false,"given":"Weicong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California, Merced, Merced, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7700-2798","authenticated-orcid":false,"given":"Duo","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California, Merced, Merced, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5820-6533","authenticated-orcid":false,"given":"Kibaek","family":"Kim","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Chicago, Illinois, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7773-7826","authenticated-orcid":false,"given":"Guanpeng","family":"Li","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9935-5674","authenticated-orcid":false,"given":"Sheng","family":"Di","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Chicago, Illinois, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-8905","authenticated-orcid":false,"given":"Xiaoyi","family":"Lu","sequence":"additional","affiliation":[{"name":"University of California, Merced, Merced, California, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv","author":"Alexey Dosovitskiy","year":"2010","unstructured":"Dosovitskiy Alexey. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv: 2010.11929 (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"FedCo: A Federated Learning Controller for Content Management in Multi-Party Edge Systems. In 2021 International Conference on Computer Communications and Networks (ICCCN). IEEE, 1\u20139.","author":"Balasubramanian Venkatraman","year":"2021","unstructured":"Venkatraman Balasubramanian, Moayad Aloqaily, and Martin Reisslein. 2021. FedCo: A Federated Learning Controller for Content Management in Multi-Party Edge Systems. In 2021 International Conference on Computer Communications and Networks (ICCCN). IEEE, 1\u20139."},{"key":"e_1_3_2_1_3_1","volume-title":"Nicol","author":"Banks Jerry","year":"2010","unstructured":"Jerry Banks, John S. Carson, Barry L. Nelson, and David M. Nicol. 2010. Discrete-Event System Simulation. (2010)."},{"key":"e_1_3_2_1_4_1","volume-title":"Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, et al.","author":"Beutel Daniel J","year":"2020","unstructured":"Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, et al. 2020. Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390 (2020)."},{"key":"e_1_3_2_1_5_1","first-page":"99","article-title":"On a Measure of Divergence between Two Statistical Populations Defined by Their Probability Distributions","volume":"35","author":"Bhattacharyya Anil","year":"1943","unstructured":"Anil Bhattacharyya. 1943. On a Measure of Divergence between Two Statistical Populations Defined by Their Probability Distributions. Bulletin of the Calcutta Mathematical Society 35 (1943), 99\u2013109.","journal-title":"Bulletin of the Calcutta Mathematical Society"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12189124"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCGRID.2001.923223"},{"key":"e_1_3_2_1_8_1","volume-title":"SimGrid: A Generic Framework for Large-Scale Distributed Experiments. In Tenth International Conference on Computer Modeling and Simulation (uksim","author":"Casanova Henri","year":"2008","unstructured":"Henri Casanova, Arnaud Legrand, and Martin Quinson. 2008. SimGrid: A Generic Framework for Large-Scale Distributed Experiments. In Tenth International Conference on Computer Modeling and Simulation (uksim 2008). IEEE, 126\u2013131."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3239542"},{"key":"e_1_3_2_1_10_1","volume-title":"SC25: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE.","author":"Chen Zhonghao","year":"2025","unstructured":"Zhonghao Chen, Yuke Li, Duo Zhang, and Xiaoyi Lu. 2025. Can Long-Haul RDMA Benefit Federated Learning?. In SC25: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE."},{"key":"e_1_3_2_1_11_1","volume-title":"Fed-Comm: Understanding Communication Protocols for Edge-Based Federated Learning. In 2022 IEEE\/ACM 15th International Conference on Utility and Cloud Computing (UCC). IEEE, 71\u201381","author":"Cleland Gary","year":"2022","unstructured":"Gary Cleland, Di Wu, Rehmat Ullah, and Blesson Varghese. 2022. Fed-Comm: Understanding Communication Protocols for Edge-Based Federated Learning. In 2022 IEEE\/ACM 15th International Conference on Utility and Cloud Computing (UCC). IEEE, 71\u201381."},{"key":"e_1_3_2_1_12_1","unstructured":"Intel Corporation. 2024. Intel VTune Profiler. https:\/\/www.intel.com\/content\/www\/us\/en\/developer\/tools\/oneapi\/vtune-profiler.html. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_13_1","unstructured":"NVIDIA Corporation. 2024. NVIDIA Nsight Compute. https:\/\/developer.nvidia.com\/nsight-compute. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2024.3360081"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3532577.3532591"},{"key":"e_1_3_2_1_16_1","unstructured":"Twitter Engineering. 2024. rpc-perf: RPC Performance Benchmarking Tool. https:\/\/github.com\/twitter\/rpc-perf. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_17_1","unstructured":"Google. 2024. gRPC Benchmarks. https:\/\/github.com\/grpc\/grpc\/blob\/master\/doc\/benchmarks.md. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_18_1","unstructured":"Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang Hongyi Wang Xiaoyang Wang Praneeth Vepakomma Abhishek Singh Hang Qiu et al. 2020. FedML: A Research Library and Benchmark for Federated Machine Learning. arXiv preprint arXiv:2007.13518 (2020)."},{"key":"e_1_3_2_1_19_1","volume-title":"Songze Li, and Giuseppe Caire.","author":"Jahani-Nezhad Tayyebeh","year":"2022","unstructured":"Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li, and Giuseppe Caire. 2022. SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees. In 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 103\u2013108."},{"key":"e_1_3_2_1_20_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and Open Problems in Federated Learning. Foundations and trends\u00ae in machine learning 14, 1\u20132 (2021), 1\u2013210."},{"key":"e_1_3_2_1_21_1","volume-title":"Scaffold: Stochastic Controlled Averaging for Federated Learning. In International conference on machine learning. PMLR, 5132\u20135143","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020. Scaffold: Stochastic Controlled Averaging for Federated Learning. In International conference on machine learning. PMLR, 5132\u20135143."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3731545.3731571"},{"key":"e_1_3_2_1_23_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning Multiple Layers of Features from Tiny Images. (2009)."},{"key":"e_1_3_2_1_24_1","volume-title":"On Information and Sufficiency. The annals of mathematical statistics 22, 1","author":"Kullback Solomon","year":"1951","unstructured":"Solomon Kullback and Richard A Leibler. 1951. On Information and Sufficiency. The annals of mathematical statistics 22, 1 (1951), 79\u201386."},{"key":"e_1_3_2_1_25_1","volume-title":"3rd IEEE\/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings. IEEE, 138\u2013145","author":"Legrand Arnaud","year":"2003","unstructured":"Arnaud Legrand, Loris Marchal, and Henri Casanova. 2003. Scheduling Distributed Applications: The SimGrid Simulation Framework. In CCGrid 2003. 3rd IEEE\/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings. IEEE, 138\u2013145."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3603165.3607364"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3312369"},{"key":"e_1_3_2_1_28_1","volume-title":"Accelerating Lossy and Lossless Compression on Emerging Bluefield DPU Architectures. In 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 373\u2013385","author":"Li Yuke","year":"2024","unstructured":"Yuke Li, Arjun Kashyap, Weicong Chen, Yanfei Guo, and Xiaoyi Lu. 2024. Accelerating Lossy and Lossless Compression on Emerging Bluefield DPU Architectures. In 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 373\u2013385."},{"key":"e_1_3_2_1_29_1","volume-title":"FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler. arXiv preprint arXiv:2309.14675","author":"Li Zilinghan","year":"2023","unstructured":"Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr Kindratenko, Eliu A Huerta, Kibaek Kim, and Ravi Madduri. 2023. FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler. arXiv preprint arXiv:2309.14675 (2023)."},{"key":"e_1_3_2_1_30_1","volume-title":"Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework. arXiv preprint arXiv:2409.11585","author":"Li Zilinghan","year":"2024","unstructured":"Zilinghan Li, Shilan He, Ze Yang, Minseok Ryu, Kibaek Kim, and Ravi Madduri. 2024. Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework. arXiv preprint arXiv:2409.11585 (2024)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.61115"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3219485"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3399649"},{"key":"e_1_3_2_1_34_1","first-page":"1080","article-title":"Like Attracts Like: Personalized Federated Learning in Decentralized Edge Computing","volume":"23","author":"Ma Zhenguo","year":"2022","unstructured":"Zhenguo Ma, Yang Xu, Hongli Xu, Jianchun Liu, and Yinxing Xue. 2022. Like Attracts Like: Personalized Federated Learning in Decentralized Edge Computing. IEEE Transactions on Mobile Computing 23, 2 (2022), 1080\u20131096.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3378351"},{"key":"e_1_3_2_1_36_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient Learning of Deep Networks from Decentralized Data. In Artificial intelligence and statistics. PMLR 1273\u20131282."},{"key":"e_1_3_2_1_37_1","volume-title":"Ray: A Distributed Framework for Emerging AI Applications. In 13th USENIX symposium on operating systems design and implementation (OSDI 18)","author":"Moritz Philipp","year":"2018","unstructured":"Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, et al. 2018. Ray: A Distributed Framework for Emerging AI Applications. In 13th USENIX symposium on operating systems design and implementation (OSDI 18). 561\u2013577."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412771"},{"key":"e_1_3_2_1_39_1","volume-title":"Federated Learning with Buffered Asynchronous Aggregation. In International Conference on Artificial Intelligence and Statistics. PMLR, 3581\u20133607","author":"Nguyen John","year":"2022","unstructured":"John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba. 2022. Federated Learning with Buffered Asynchronous Aggregation. In International Conference on Artificial Intelligence and Statistics. PMLR, 3581\u20133607."},{"key":"e_1_3_2_1_40_1","unstructured":"University of Illinois Urbana-Champaign. 2025. National Center for Supercomputing Applications - Delta. https:\/\/allocations.access-ci.org\/resources\/delta.ncsa.access-ci.org. Accessed: 2025-10-23."},{"key":"e_1_3_2_1_41_1","volume-title":"PAPI: Performance Application Programming Interface. https:\/\/icl.utk.edu\/papi\/. Accessed: 2024-02-27.","author":"University of Tennessee Innovative Computing Laboratory.","year":"2024","unstructured":"University of Tennessee Innovative Computing Laboratory. 2024. PAPI: Performance Application Programming Interface. https:\/\/icl.utk.edu\/papi\/. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_42_1","unstructured":"PaddlePaddle. 2020. FL-Mobile Simulator. https:\/\/github.com\/PaddlePaddle\/PaddleFL\/tree\/master\/python\/paddle_fl\/mobile."},{"key":"e_1_3_2_1_43_1","volume-title":"High-Performance Big Data Computing","author":"Panda Dhabaleswar K","unstructured":"Dhabaleswar K Panda, Xiaoyi Lu, and Dipti Shankar. 2022. High-Performance Big Data Computing. MIT Press."},{"key":"e_1_3_2_1_44_1","unstructured":"Parallel and Distributed Systems Laboratory (PADSYS Lab). 2025. SR-APPFL Project. https:\/\/sites.google.com\/view\/sr-appfl\/home. Accessed: 2025-10-23."},{"key":"e_1_3_2_1_45_1","unstructured":"python. 2025. The Python Profilers. https:\/\/docs.python.org\/3\/library\/profile.html. Accessed: 2025-10-23."},{"key":"e_1_3_2_1_46_1","volume-title":"Early Experience in Characterizing Training Large Language Models on Modern HPC Clusters. In SC23: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE.","author":"Qi Hao","year":"2023","unstructured":"Hao Qi, Liuyao Dai, Weicong Chen, and Xiaoyi Lu. 2023. Early Experience in Characterizing Training Large Language Models on Modern HPC Clusters. In SC23: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE."},{"key":"e_1_3_2_1_47_1","unstructured":"Meta Research. 2021. Federated Learning Simulator (FLSim). https:\/\/github.com\/facebookresearch\/FLSim."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Joe Rickerby. 2016. PyInstrument. https:\/\/github.com\/joerick\/pyinstrument. Accessed: 2025-10-23.","DOI":"10.1353\/bio.2016.0023"},{"key":"e_1_3_2_1_49_1","volume-title":"Ananda Theertha Suresh, and Ke Wu","author":"Ro Jae Hun","year":"2021","unstructured":"Jae Hun Ro, Ananda Theertha Suresh, and Ke Wu. 2021. FedJAX: Federated Learning Simulation with JAX. arXiv preprint arXiv:2108.02117 (2021)."},{"key":"e_1_3_2_1_50_1","volume-title":"APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning. In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 1074\u20131083","author":"Ryu Minseok","year":"2022","unstructured":"Minseok Ryu, Youngdae Kim, Kibaek Kim, and Ravi K Madduri. 2022. APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning. In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 1074\u20131083."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.9"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3309633"},{"key":"e_1_3_2_1_53_1","volume-title":"Edge Computing: Vision and Challenges","author":"Shi Weisong","year":"2016","unstructured":"Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge Computing: Vision and Challenges. IEEE internet of things journal 3, 5 (2016), 637\u2013646."},{"key":"e_1_3_2_1_54_1","first-page":"3353","article-title":"Edge Computing","volume":"19","author":"Sitt\u00f3n-Candanedo In\u00e9s","year":"2019","unstructured":"In\u00e9s Sitt\u00f3n-Candanedo, Ricardo S Alonso, \u00d3scar Garc\u00eda, Lilia Mu\u00f1oz, and Sara Rodr\u00edguez-Gonz\u00e1lez. 2019. Edge Computing, IoT and Social Computing in Smart Energy Scenarios. Sensors 19, 15 (2019), 3353.","journal-title":"IoT and Social Computing in Smart Energy Scenarios. Sensors"},{"key":"e_1_3_2_1_55_1","volume-title":"Ananda Theertha Suresh, and H Brendan McMahan","author":"Sun Ziteng","year":"2019","unstructured":"Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019. Can You Really Backdoor Federated Learning? arXiv preprint arXiv:1911.07963 (2019)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3120724"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"crossref","unstructured":"R Udayakumar B Mahesh R Sathiyakala Kavitha Thandapani Abhishek Choubey Azizbek Khurramov Laith H Alzubaidi and Jajimoggala Sravanthi. 2023. An Integrated Deep Learning and Edge Computing Framework for Intelligent Energy Management in IoT-Based Smart Cities. In 2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD). IEEE 32\u201338.","DOI":"10.1109\/ICTEASD57136.2023.10585232"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/JRFID.2023.3279329"},{"key":"e_1_3_2_1_59_1","unstructured":"Ohio State University. 2024. OSU Micro-Benchmarks. https:\/\/mvapich.cse.ohio-state.edu\/benchmarks\/. Accessed: 2024-02-27."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3712285.3759827"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/1498765.1498785"},{"key":"e_1_3_2_1_62_1","volume-title":"Asynchronous Federated Optimization. arXiv preprint arXiv:1903.03934","author":"Xie Cong","year":"2019","unstructured":"Cong Xie, Sanmi Koyejo, and Indranil Gupta. 2019. Asynchronous Federated Optimization. arXiv preprint arXiv:1903.03934 (2019)."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIC56439.2022.00012"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2025.3593896"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3118609"},{"key":"e_1_3_2_1_66_1","first-page":"3053","article-title":"MIPD: An Adaptive Gradient Sparsification Framework for Distributed DNNs Training","volume":"33","author":"Zhang Zhaorui","year":"2022","unstructured":"Zhaorui Zhang and Choli Wang. 2022. MIPD: An Adaptive Gradient Sparsification Framework for Distributed DNNs Training. IEEE Transactions on Parallel and Distributed Systems 33, 11 (2022), 3053\u20133066.","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"e_1_3_2_1_67_1","volume-title":"Mobile Edge Computing, Blockchain and Reputation-Based Crowd-sourcing IoT Federated Learning: A Secure, Decentralized and Privacy-Preserving System. arXiv preprint arXiv:1906.10893","author":"Zhao Yang","year":"2019","unstructured":"Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, and Dusit Niyato. 2019. Mobile Edge Computing, Blockchain and Reputation-Based Crowd-sourcing IoT Federated Learning: A Secure, Decentralized and Privacy-Preserving System. arXiv preprint arXiv:1906.10893 (2019), 2327\u20134662."}],"event":{"name":"SEC '25: Tenth ACM\/IEEE Symposium on Edge Computing","location":"the Hilton Arlington National Landing Arlington VA USA","acronym":"SEC '25","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","IEEE Computer Society"]},"container-title":["Proceedings of the Tenth ACM\/IEEE Symposium on Edge Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769102.3770613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:01:03Z","timestamp":1764777663000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769102.3770613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":67,"alternative-id":["10.1145\/3769102.3770613","10.1145\/3769102"],"URL":"https:\/\/doi.org\/10.1145\/3769102.3770613","relation":{},"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}