{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:05Z","timestamp":1772908625267,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2024,12,2]]},"DOI":"10.1145\/3652892.3700778","type":"proceedings-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T19:36:13Z","timestamp":1732736173000},"page":"367-378","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Spyker: Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0137-9863","authenticated-orcid":false,"given":"Yuncong","family":"Zuo","sequence":"first","affiliation":[{"name":"Delft University of Technology, Delft, Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5209-6161","authenticated-orcid":false,"given":"Bart","family":"Cox","sequence":"additional","affiliation":[{"name":"Delft University of Technology, Delft, Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4228-6735","authenticated-orcid":false,"given":"Lydia Y.","family":"Chen","sequence":"additional","affiliation":[{"name":"Delft University of Technology and Universit\u00e9 de Neuchatel, Delft, Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9143-3984","authenticated-orcid":false,"given":"J\u00e9r\u00e9mie","family":"Decouchant","sequence":"additional","affiliation":[{"name":"Delft University of Technology, Delft, Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","unstructured":"Mehdi Salehi Heydar Abad Emre Ozfatura Deniz G\u00fcnd\u00fcz and \u00d6zg\u00fcr Er\u00e7etin. 2020. Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks. In ICASSP. 8866--8870. 10.1109\/ICASSP40776.2020.9054634","DOI":"10.1109\/ICASSP40776.2020.9054634"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2018.10.026"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2211.16162"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJMEDINF.2018.01.007"},{"key":"e_1_3_2_1_5_1","first-page":"41","article-title":"Towards Geo-Distributed Machine Learning","volume":"40","author":"Cano Ignacio","year":"2017","unstructured":"Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, and Arvind Krishnamurthy. 2017. Towards Geo-Distributed Machine Learning. Data Engineering Bulletin 40, 4 (2017), 41--59. http:\/\/sites.computer.org\/debull\/A17dec\/p41.pdf","journal-title":"Data Engineering Bulletin"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","unstructured":"Yujing Chen Yue Ning Martin Slawski and Huzefa Rangwala. 2020. Asynchronous Online Federated Learning for Edge Devices with Non-IID Data. In BigData. 15--24. 10.1109\/BIGDATA50022.2020.9378161","DOI":"10.1109\/BIGDATA50022.2020.9378161"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.DCAN.2021.04.001"},{"key":"e_1_3_2_1_8_1","volume-title":"Project Adam: Building an Efficient and Scalable Deep Learning Training System. In OSDI. 571--582. https:\/\/www.usenix.org\/conference\/osdi14\/technical-sessions\/presentation\/chilimbi","author":"Chilimbi Trishul M.","year":"2014","unstructured":"Trishul M. Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman. 2014. Project Adam: Building an Efficient and Scalable Deep Learning Training System. In OSDI. 571--582. https:\/\/www.usenix.org\/conference\/osdi14\/technical-sessions\/presentation\/chilimbi"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Bart Cox Lydia Y. Chen and J\u00e9r\u00e9mie Decouchant. 2022. Aergia: leveraging heterogeneity in federated learning systems. In Middleware. 107--120. 10.1145\/3528535.3565238","DOI":"10.1145\/3528535.3565238"},{"key":"e_1_3_2_1_10_1","unstructured":"Bart Cox Abele M\u0103lan Lydia Y. Chen and J\u00e9r\u00e9mie Decouchant. 2024. Asynchronous Byzantine Federated Learning. arXiv:2406.01438 [cs.LG] https:\/\/arxiv.org\/abs\/2406.01438"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2901318.2901323"},{"key":"e_1_3_2_1_12_1","volume-title":"Ng","author":"Dean Jeffrey","year":"2012","unstructured":"Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, and Andrew Y. Ng. 2012. Large Scale Distributed Deep Networks. (2012), 1232--1240. https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/6aca97005c68f1206823815f66102863-Abstract.html"},{"key":"e_1_3_2_1_13_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"2620","author":"Dennis Don Kurian","year":"2021","unstructured":"Don Kurian Dennis, Tian Li, and Virginia Smith. 2021. Heterogeneity for the Win: One-Shot Federated Clustering. In ICML (Proceedings of Machine Learning Research, Vol. 139). 2611--2620. http:\/\/proceedings.mlr.press\/v139\/dennis21a.html"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","unstructured":"Jiangshan Hao Yanchao Zhao and Jiale Zhang. 2020. Time Efficient Federated Learning with Semi-asynchronous Communication. In ICPADS. 156--163. 10.1109\/ICPADS51040.2020.00030","DOI":"10.1109\/ICPADS51040.2020.00030"},{"key":"e_1_3_2_1_15_1","volume-title":"Federated Learning for Mobile Keyboard Prediction. CoRR abs\/1811.03604","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard, Kanishka Rao, Rajiv Mathews, Fran\u00e7oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. CoRR abs\/1811.03604 (2018). arXiv:1811.03604 http:\/\/arxiv.org\/abs\/1811.03604"},{"key":"e_1_3_2_1_16_1","volume-title":"Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds. In NSDI. 629--647. https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/hsieh","author":"Hsieh Kevin","year":"2017","unstructured":"Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, and Onur Mutlu. 2017. Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds. In NSDI. 629--647. https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/hsieh"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","unstructured":"Zhiqi Huang Fenglin Liu and Yuexian Zou. 2020. Federated Learning for Spoken Language Understanding. In COLING. 3467--3478. 10.18653\/V1\/2020.COLING-MAIN.310","DOI":"10.18653\/V1\/2020.COLING-MAIN.310"},{"key":"e_1_3_2_1_18_1","volume-title":"Jordan","author":"Jaggi Martin","year":"2014","unstructured":"Martin Jaggi, Virginia Smith, Martin Tak\u00e1c, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I. Jordan. 2014. Communication-Efficient Distributed Dual Coordinate Ascent. (2014), 3068--3076. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/894b77f805bd94d292574c38c5d628d5-Abstract.html"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","unstructured":"Li Li Haoyi Xiong Zhishan Guo Jun Wang and Cheng-Zhong Xu. 2019. SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System. In RTSS. 406--418. 10.1109\/RTSS46320.2019.00043","DOI":"10.1109\/RTSS46320.2019.00043"},{"key":"e_1_3_2_1_20_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","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. In MLSys. https:\/\/proceedings.mlsys.org\/paper_files\/paper\/2020\/hash\/1f5fe83998a09396ebe6477d9475ba0c-Abstract.html"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.MEDIA.2020.101765"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"e_1_3_2_1_23_1","volume-title":"A Functional Approximation Based Distributed Learning Algorithm. CoRR abs\/1310.8418","author":"Mahajan Dhruv","year":"2013","unstructured":"Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan, and L\u00e9on Bottou. 2013. A Functional Approximation Based Distributed Learning Algorithm. CoRR abs\/1310.8418 (2013). arXiv:1310.8418 http:\/\/arxiv.org\/abs\/1310.8418"},{"key":"e_1_3_2_1_24_1","volume-title":"AISTATS (Proceedings of Machine Learning Research","volume":"1282","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 AISTATS (Proceedings of Machine Learning Research, Vol. 54). 1273--1282. http:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3200016"},{"key":"e_1_3_2_1_26_1","volume-title":"Chen","author":"Rashad Mohamed","year":"2024","unstructured":"Mohamed Rashad, Zilong Zhao, Jeremie Decouchant, and Lydia Y. Chen. 2024. TabVFL: Improving Latent Representation in Vertical Federated Learning. CoRR (2024). arXiv:2404.17990 [cs.LG] https:\/\/arxiv.org\/abs\/2404.17990"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/S41746-020-00323-1"},{"key":"e_1_3_2_1_28_1","unstructured":"Amazon Web Services. 2024. AWS Latency Monitoring. https:\/\/www.cloudping.co\/grid"},{"key":"e_1_3_2_1_29_1","unstructured":"Aditya Shankar Lydia Y. Chen J\u00e9r\u00e9mie Decouchant Dimitra Gkorou and Rihan Hai. 2024. Share Your Secrets for Privacy! Confidential Forecasting with Vertical Federated Learning. arXiv:2405.20761 [cs.LG] https:\/\/arxiv.org\/abs\/2405.20761"},{"key":"e_1_3_2_1_30_1","unstructured":"Virginia Smith Chao-Kai Chiang Maziar Sanjabi and Ameet Talwalkar. 2017. Federated Multi-Task Learning. In NeurIPS. 4424--4434. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2205.13797"},{"key":"e_1_3_2_1_32_1","volume-title":"MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering. arXiv:2208.10161 [cs.CR] https:\/\/arxiv.org\/abs\/2208.10161","author":"Wang Rui","year":"2023","unstructured":"Rui Wang, Xingkai Wang, Huanhuan Chen, J\u00e9r\u00e9mie Decouchant, Stjepan Picek, Nikolaos Laoutaris, and Kaitai Liang. 2023. MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering. arXiv:2208.10161 [cs.CR] https:\/\/arxiv.org\/abs\/2208.10161"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2206.00054"},{"key":"e_1_3_2_1_34_1","volume-title":"Asynchronous Federated Optimization. CoRR abs\/1903.03934","author":"Xie Cong","year":"2019","unstructured":"Cong Xie, Sanmi Koyejo, and Indranil Gupta. 2019. Asynchronous Federated Optimization. CoRR abs\/1903.03934 (2019). arXiv:1903.03934 http:\/\/arxiv.org\/abs\/1903.03934"},{"key":"e_1_3_2_1_35_1","volume-title":"Multi-Center Federated Learning. CoRR abs\/2005.01026","author":"Xie Ming","year":"2020","unstructured":"Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, and Jing Jiang. 2020. Multi-Center Federated Learning. CoRR abs\/2005.01026 (2020). arXiv:2005.01026 https:\/\/arxiv.org\/abs\/2005.01026"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/S41666-020-00082-4"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS55811.2022.00012"},{"key":"e_1_3_2_1_38_1","volume-title":"DiSCO: Distributed Optimization for Self-Concordant Empirical Loss. In ICML (JMLR Workshop and Conference Proceedings","volume":"370","author":"Zhang Yuchen","year":"2015","unstructured":"Yuchen Zhang and Xiao Lin. 2015. DiSCO: Distributed Optimization for Self-Concordant Empirical Loss. In ICML (JMLR Workshop and Conference Proceedings, Vol. 37). 362--370. http:\/\/proceedings.mlr.press\/v37\/zhangb15.html"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","unstructured":"Chendi Zhou Hao Tian Hong Zhang Jin Zhang Mianxiong Dong and Juncheng Jia. 2021. TEA-fed: time-efficient asynchronous federated learning for edge computing. In CF. 30--37. 10.1145\/3457388.3458655","DOI":"10.1145\/3457388.3458655"},{"key":"e_1_3_2_1_40_1","volume-title":"Sky Computing: Accelerating Geo-distributed Computing in Federated Learning. CoRR abs\/2202.11836","author":"Zhu Jie","year":"2022","unstructured":"Jie Zhu, Shenggui Li, and Yang You. 2022. Sky Computing: Accelerating Geo-distributed Computing in Federated Learning. CoRR abs\/2202.11836 (2022). arXiv:2202.11836 https:\/\/arxiv.org\/abs\/2202.11836"}],"event":{"name":"Middleware '24: 25th International Middleware Conference","location":"Hong Kong Hong Kong","acronym":"Middleware '24","sponsor":["IFIP","Usenix"]},"container-title":["Proceedings of the 25th International Middleware Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652892.3700778","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652892.3700778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:53:57Z","timestamp":1750287237000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652892.3700778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":40,"alternative-id":["10.1145\/3652892.3700778","10.1145\/3652892"],"URL":"https:\/\/doi.org\/10.1145\/3652892.3700778","relation":{},"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2024-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}