{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:46:34Z","timestamp":1767339994608,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CCF-2217070, CNS-1909769"],"award-info":[{"award-number":["CCF-2217070, CNS-1909769"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF Chameleon Cloud Testbed"},{"DOI":"10.13039\/100004351","name":"Cisco Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004351","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,30]]},"DOI":"10.1145\/3620678.3624665","type":"proceedings-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T13:58:07Z","timestamp":1698760687000},"page":"341-357","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Flame"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7996-5910","authenticated-orcid":false,"given":"Harshit","family":"Daga","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5812-6074","authenticated-orcid":false,"given":"Jaemin","family":"Shin","sequence":"additional","affiliation":[{"name":"KAIST and Work done at Cisco Research"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7655-845X","authenticated-orcid":false,"given":"Dhruv","family":"Garg","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4199-2512","authenticated-orcid":false,"given":"Ada","family":"Gavrilovska","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-7019","authenticated-orcid":false,"given":"Myungjin","family":"Lee","sequence":"additional","affiliation":[{"name":"Cisco Research"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7559-8997","authenticated-orcid":false,"given":"Ramana Rao","family":"Kompella","sequence":"additional","affiliation":[{"name":"Cisco Research"}]}],"member":"320","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265--283."},{"key":"e_1_3_2_1_2_1","volume-title":"Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, and Nicholas D. Lane.","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, and Nicholas D. Lane. 2020. Flower: A Friendly Federated Learning Research Framework. https:\/\/arxiv.org\/abs\/2007.14390"},{"key":"e_1_3_2_1_3_1","first-page":"374","article-title":"Towards federated learning at scale: System design","volume":"1","author":"Bonawitz Keith","year":"2019","unstructured":"Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Kone\u010dny, Stefano Mazzocchi, Brendan McMahan, et al. 2019. Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems 1 (2019), 374--388.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_4_1","volume-title":"A survey of data mining and machine learning methods for cyber security intrusion detection","author":"Buczak Anna L","year":"2015","unstructured":"Anna L Buczak and Erhan Guven. 2015. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials 18, 2 (2015), 1153--1176."},{"key":"e_1_3_2_1_5_1","volume-title":"MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 (2015). arXiv:1512.01274 http:\/\/arxiv.org\/abs\/1512.01274"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2023.3294490"},{"key":"e_1_3_2_1_7_1","unstructured":"Randy DeFauw and Collin Cudd. December 2021. Applying Federated Learning for ML at the Edge. https:\/\/aws.amazon.com\/blogs\/architecture\/applying-federated-learning-for-ml-at-the-edge\/."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_1_9_1","volume-title":"Daniel Madrigal Diaz, Andre Manoel, and Robert Sim.","author":"Dimitriadis Dimitrios","year":"2022","unstructured":"Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal Diaz, Andre Manoel, and Robert Sim. 2022. Flute: A scalable, extensible framework for high-performance federated learning simulations. arXiv preprint arXiv:2203.13789 (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.4236\/jilsa.2017.91001"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac97d9"},{"key":"e_1_3_2_1_12_1","unstructured":"Apache Software Foundation. 2015. Apache Airflow. https:\/\/airflow.apache.org."},{"key":"e_1_3_2_1_13_1","volume-title":"Hybrid Local SGD for Federated Learning with Heterogeneous Communications. In International Conference on Learning Representations.","author":"Guo Yuanxiong","year":"2022","unstructured":"Yuanxiong Guo, Ying Sun, Rui Hu, and Yanmin Gong. 2022. Hybrid Local SGD for Federated Learning with Heterogeneous Communications. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","unstructured":"Andrew Hard Chlo\u00e9 M Kiddon Daniel Ramage Francoise Beaufays Hubert Eichner Kanishka Rao Rajiv Mathews and Sean Augenstein. 2018. Federated Learning for Mobile Keyboard Prediction. https:\/\/arxiv.org\/abs\/1811.03604"},{"key":"e_1_3_2_1_15_1","unstructured":"Stephen Hardy Wilko Henecka Hamish Ivey-Law Richard Nock Giorgio Patrini Guillaume Smith and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. https:\/\/arxiv.org\/abs\/1711.10677"},{"key":"e_1_3_2_1_16_1","unstructured":"Stephen Hardy Wilko Henecka Hamish Ivey-Law Richard Nock Giorgio Patrini Guillaume Smith and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv:1711.10677 [cs.LG]"},{"key":"e_1_3_2_1_17_1","volume-title":"Fedml: A research library and benchmark for federated machine learning. arXiv preprint arXiv:2007.13518","author":"He Chaoyang","year":"2020","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_18_1","first-page":"814","article-title":"Papaya: Practical, private, and scalable federated learning","volume":"4","author":"Huba Dzmitry","year":"2022","unstructured":"Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, et al. 2022. Papaya: Practical, private, and scalable federated learning. Proceedings of Machine Learning and Systems 4 (2022), 814--832.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-019-10063-9"},{"key":"e_1_3_2_1_20_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning.","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. 2022. FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. In Proceedings of the 39th International Conference on Machine Learning."},{"key":"e_1_3_2_1_22_1","volume-title":"Oort: Informed participant selection for scalable federated learning. arXiv preprint arXiv:2010.06081","author":"Lai Fan","year":"2020","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury. 2020. Oort: Informed participant selection for scalable federated learning. arXiv preprint arXiv:2010.06081 (2020)."},{"key":"e_1_3_2_1_23_1","volume-title":"Tara Javidi, and Farinaz Koushanfar.","author":"Lalitha Anusha","year":"2019","unstructured":"Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar. 2019. Peer-to-peer Federated Learning on Graphs. https:\/\/arxiv.org\/abs\/1901.11173"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/risks7010029"},{"key":"e_1_3_2_1_25_1","volume-title":"FLSim: An Extensible and Reusable Simulation Framework for Federated Learning. In International Conference on Simulation Tools and Techniques. Springer, 350--369","author":"Li Li","year":"2020","unstructured":"Li Li, Jun Wang, and ChengZhong Xu. 2020. FLSim: An Extensible and Reusable Simulation Framework for Federated Learning. In International Conference on Simulation Tools and Techniques. Springer, 350--369."},{"key":"e_1_3_2_1_26_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems 2 (2020), 429--450.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_27_1","volume-title":"Fair Resource Allocation in Federated Learning. In 8th International Conference on Learning Representations, ICLR 2020","author":"Li Tian","year":"2020","unstructured":"Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2020. Fair Resource Allocation in Federated Learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=ByexElSYDr"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318216.3363304"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3003744"},{"key":"e_1_3_2_1_31_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."},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.","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 Proceedings of The 25th International Conference on Artificial Intelligence and Statistics."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"e_1_3_2_1_34_1","unstructured":"Nvidia. 2018. Nvidia Clara. https:\/\/www.nvidia.com\/en-us\/clara\/"},{"key":"e_1_3_2_1_35_1","unstructured":"European Parliament and Council of the European Union. 2018. EU General Data Protection Regulation. https:\/\/eugdpr.org\/."},{"key":"e_1_3_2_1_36_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2008.09.002"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_39_1","unstructured":"Sashank Reddi Zachary Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Kone\u010dn\u00fd Sanjiv Kumar and H. Brendan McMahan. 2020. Adaptive Federated Optimization. https:\/\/arxiv.org\/abs\/2003.00295"},{"key":"e_1_3_2_1_40_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_41_1","unstructured":"Theo Ryffel Andrew Trask Morten Dahl Bobby Wagner Jason Mancuso Daniel Rueckert and Jonathan Passerat-Palmbach. 2018. A generic framework for privacy preserving deep learning. https:\/\/arxiv.org\/abs\/1811.04017"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538917"},{"key":"e_1_3_2_1_43_1","volume-title":"Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. In AAAI Conference on Artificial Intelligence.","author":"Wang Jiayi","year":"2020","unstructured":"Jiayi Wang, Shiqiang Wang, Rong-Rong Chen, and Mingyue Ji. 2020. Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1080\/21693277.2016.1192517"},{"key":"e_1_3_2_1_45_1","unstructured":"Timothy Yang Galen Andrew Hubert Eichner Haicheng Sun Wei Li Nicholas Kong Daniel Ramage and Fran\u00e7oise Beaufays. 2018. Applied Federated Learning: Improving Google Keyboard Query Suggestions. https:\/\/arxiv.org\/abs\/1812.02903"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"}],"event":{"name":"SoCC '23: ACM Symposium on Cloud Computing","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Santa Cruz CA USA","acronym":"SoCC '23"},"container-title":["Proceedings of the 2023 ACM Symposium on Cloud Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620678.3624665","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3620678.3624665","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3620678.3624665","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:55:10Z","timestamp":1755878110000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620678.3624665"}},"subtitle":["Simplifying Topology Extension in Federated Learning"],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":46,"alternative-id":["10.1145\/3620678.3624665","10.1145\/3620678"],"URL":"https:\/\/doi.org\/10.1145\/3620678.3624665","relation":{},"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"2023-10-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}