{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:12:37Z","timestamp":1783699957152,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":108,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["NSF-OAC-23313738,NSF-CAREER-23313737,NSF-SPX-2202859,NSF-CNS-2322919"],"award-info":[{"award-number":["NSF-OAC-23313738,NSF-CAREER-23313737,NSF-SPX-2202859,NSF-CNS-2322919"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,22]]},"DOI":"10.1145\/3627703.3629575","type":"proceedings-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T06:28:28Z","timestamp":1713421708000},"page":"182-199","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Totoro: A Scalable Federated Learning Engine for the Edge"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6621-4907","authenticated-orcid":false,"given":"Cheng-Wei","family":"Ching","sequence":"first","affiliation":[{"name":"University of California, Santa Cruz, Santa Cruz, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8188-8211","authenticated-orcid":false,"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4378-1691","authenticated-orcid":false,"given":"Taehwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0149-7509","authenticated-orcid":false,"given":"Bo","family":"Ji","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-2898","authenticated-orcid":false,"given":"Qingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Louisiana State University, Baton Rouge, LA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6538-2888","authenticated-orcid":false,"given":"Dilma","family":"Da Silva","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7222-5507","authenticated-orcid":false,"given":"Liting","family":"Hu","sequence":"additional","affiliation":[{"name":"University of California, Santa Cruz and Virginia Tech, Santa Cruz, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Bittorrent. http:\/\/www.bittorrent.com\/."},{"key":"e_1_3_2_2_2_1","volume-title":"Iot-enabled software, worldwide","author":"Forecast","year":"2019","unstructured":"Forecast: Iot-enabled software, worldwide, 2019--2025, gartner. https:\/\/www.gartner.com\/en\/documents\/4009207."},{"key":"e_1_3_2_2_3_1","unstructured":"Freenet: The free network. https:\/\/freenetproject.org\/."},{"key":"e_1_3_2_2_4_1","unstructured":"Istio. https:\/\/istio.io\/latest\/."},{"key":"e_1_3_2_2_5_1","unstructured":"Keras. https:\/\/keras.io\/."},{"key":"e_1_3_2_2_6_1","unstructured":"Microsoft cognitive toolkit (cntk) an open source deep-learning toolkit. https:\/\/github.com\/Microsoft\/CNTK\/."},{"key":"e_1_3_2_2_7_1","unstructured":"OpenFL - an open-source framework for federated learning. https:\/\/openfl.readthedocs.io\/."},{"key":"e_1_3_2_2_8_1","unstructured":"PaddleFL: Paddle federated learning. https:\/\/github.com\/PaddlePaddle\/PaddleFL."},{"key":"e_1_3_2_2_9_1","unstructured":"Pastry. https:\/\/www.freepastry.org\/FreePastry\/."},{"key":"e_1_3_2_2_10_1","unstructured":"Plaidml - a framework for making deep learning work everywhere. https:\/\/github.com\/plaidml\/plaidml\/."},{"key":"e_1_3_2_2_11_1","unstructured":"PySyft: A library for easy federated learning. https:\/\/github.com\/OpenMined\/PySyft."},{"key":"e_1_3_2_2_12_1","unstructured":"Spark streaming. https:\/\/spark.apache.org\/streaming\/."},{"key":"e_1_3_2_2_13_1","unstructured":"Storj.io. http:\/\/storj.io\/."},{"key":"e_1_3_2_2_14_1","unstructured":"Symbiotic lab. https:\/\/symbioticlab.org\/."},{"key":"e_1_3_2_2_15_1","unstructured":"Tensorflow federated: Machine learning on decentralized data. https:\/\/www.tensorflow.org\/federated."},{"key":"e_1_3_2_2_16_1","unstructured":"Theano. https:\/\/github.com\/Theano\/Theano."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054634"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5706"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403097"},{"key":"e_1_3_2_2_20_1","volume-title":"Advances in Neural Information Processing Systems","author":"Alistarh Dan","year":"2017","unstructured":"Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. Qsgd: Communication-efficient sgd via gradient quantization and encoding. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437378.3444367"},{"key":"e_1_3_2_2_22_1","volume-title":"Can Karakus, and Suhas Diggavi. Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc","author":"Basu Debraj","year":"2019","unstructured":"Debraj Basu, Deepesh Data, Can Karakus, and Suhas Diggavi. Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019."},{"key":"e_1_3_2_2_23_1","first-page":"473","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Bellet Aur\u00e9lien","year":"2018","unstructured":"Aur\u00e9lien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi. Personalized and private peer-to-peer machine learning. In International Conference on Artificial Intelligence and Statistics, pages 473--481. PMLR, 2018."},{"key":"e_1_3_2_2_24_1","volume-title":"International Conference on Learning Representations","author":"Bernstein Jeremy","year":"2019","unstructured":"Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, and Anima Anandkumar. signSGD with majority vote is communication efficient and fault tolerant. In International Conference on Learning Representations, 2019."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2011.2159844"},{"key":"e_1_3_2_2_26_1","first-page":"374","volume-title":"Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. Towards federated learning at scale: System design","author":"Bonawitz Keith","year":"2019","unstructured":"Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chlo\u00e9 Kiddon, Jakub Kone\u010dn\u00fd Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. Towards federated learning at scale: System design. In A. Talwalkar, V. Smith, and M. Zaharia, editors, Proceedings of Machine Learning and Systems, volume 1, pages 374--388, 2019."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000024"},{"key":"e_1_3_2_2_28_1","volume-title":"Peter Wu, Tian Li, Jakub Kone\u010dn\u00fd, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Kone\u010dn\u00fd, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018."},{"key":"e_1_3_2_2_29_1","volume-title":"Peter Wu, Tian Li, Jakub Kone\u010dny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Kone\u010dny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2002.803069"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/945445.945474"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1133373.1133399"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20","author":"Chen Chia-Yu","year":"2020","unstructured":"Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, and Kailash Gopalakrishnan. Scalecom: Scalable sparsified gradient compression for communication-efficient distributed training. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc."},{"key":"e_1_3_2_2_36_1","series-title":"Proceedings of Machine Learning Research","first-page":"151","volume-title":"Proceedings of the 30th International Conference on Machine Learning","author":"Chen Wei","year":"2013","unstructured":"Wei Chen, Yajun Wang, and Yang Yuan. Combinatorial multi-armed bandit: General framework and applications. In Sanjoy Dasgupta and David McAllester, editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, pages 151--159, Atlanta, Georgia, USA, 17-19 Jun 2013. PMLR."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53357-4_8"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3188456"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2011.2181864"},{"key":"e_1_3_2_2_43_1","volume-title":"Advances in Neural Information Processing Systems Workshop: Machine Learning on the Phone and other Consumer Devices","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. In Advances in Neural Information Processing Systems Workshop: Machine Learning on the Phone and other Consumer Devices, 2017."},{"key":"e_1_3_2_2_44_1","series-title":"Proceedings of Machine Learning Research","first-page":"2521","volume-title":"Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh. Shuffled model of differential privacy in federated learning","author":"Girgis Antonious","year":"2021","unstructured":"Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh. Shuffled model of differential privacy in federated learning. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 2521--2529. PMLR, 13-15 Apr 2021."},{"key":"e_1_3_2_2_45_1","volume-title":"Byzantine fault-tolerance in peer-to-peer distributed gradient-descent. arXiv preprint arXiv:2101.12316","author":"Gupta Nirupam","year":"2021","unstructured":"Nirupam Gupta and Nitin H Vaidya. Byzantine fault-tolerance in peer-to-peer distributed gradient-descent. arXiv preprint arXiv:2101.12316, 2021."},{"key":"e_1_3_2_2_46_1","series-title":"Proceedings of Machine Learning Research","first-page":"2350","volume-title":"Aryan Mokhtari, and Mehrdad Mahdavi. Federated learning with compression: Unified analysis and sharp guarantees","author":"Haddadpour Farzin","year":"2021","unstructured":"Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi. Federated learning with compression: Unified analysis and sharp guarantees. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 2350--2358. PMLR, 13-15 Apr 2021."},{"key":"e_1_3_2_2_47_1","volume-title":"Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Fran\u00e7oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604, 2018."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_49_1","first-page":"629","volume-title":"Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI'17","author":"Hsieh Kevin","year":"2017","unstructured":"Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, and Onur Mutlu. Gaia: Geo-distributed machine learning approaching lan speeds. In Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI'17, page 629--647, USA, 2017. USENIX Association."},{"key":"e_1_3_2_2_50_1","first-page":"814","volume-title":"Practical, private, and scalable federated learning","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, Kaikai Wang, Anthony Shoumikhin, Jesik Min, and Mani Malek. Papaya: Practical, private, and scalable federated learning. In D. Marculescu, Y. Chi, and C. Wu, editors, Proceedings of Machine Learning and Systems, volume 4, pages 814--832, 2022."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900649"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"e_1_3_2_2_53_1","series-title":"Proceedings of Machine Learning Research","first-page":"11814","volume-title":"Kamalika Chaudhuri","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. FedScale: Benchmarking model and system performance of federated learning at scale. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 11814--11827. PMLR, 17-23 Jul 2022."},{"key":"e_1_3_2_2_54_1","first-page":"273","volume-title":"Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation, NSDI'20","author":"Lai Fan","year":"2020","unstructured":"Fan Lai, Jie You, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. Sol: Fast distributed computation over slow networks. In Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation, NSDI'20, page 273--288, USA, 2020. USENIX Association."},{"key":"e_1_3_2_2_55_1","first-page":"19","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. Oort: Efficient federated learning via guided participant selection. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), pages 19--35. USENIX Association, July 2021."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03596-9_15"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-8858(85)90002-8"},{"key":"e_1_3_2_2_58_1","volume-title":"Advances in Neural Information Processing Systems Workshop on Bayesian Deep Learning","volume":"2","author":"Lalitha Anusha","year":"2018","unstructured":"Anusha Lalitha, Shubhanshu Shekhar, Tara Javidi, and Farinaz Koushanfar. Fully decentralized federated learning. In Advances in Neural Information Processing Systems Workshop on Bayesian Deep Learning, volume 2, 2018."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401"},{"key":"e_1_3_2_2_60_1","volume-title":"Proceedings of Machine learning and systems, 2:429--450","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429--450, 2020."},{"key":"e_1_3_2_2_61_1","volume-title":"International Conference on Learning Representations","author":"Li Tian","year":"2020","unstructured":"Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. Fair resource allocation in federated learning. In International Conference on Learning Representations, 2020."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525485"},{"key":"e_1_3_2_2_63_1","volume-title":"International Conference on Learning Representations","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, and Martin Jaggi. Don't use large minibatches, use local sgd. In International Conference on Learning Representations, 2020."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3190512"},{"key":"e_1_3_2_2_66_1","volume-title":"IBM federated learning: an enterprise framework white paper v0.1. arXiv preprint arXiv:2007.10987","author":"Ludwig Heiko","year":"2020","unstructured":"Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al. IBM federated learning: an enterprise framework white paper v0.1. arXiv preprint arXiv:2007.10987, 2020."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_2_2_68_1","first-page":"53","volume-title":"IPTPS '01","author":"Maymounkov Petar","year":"2002","unstructured":"Petar Maymounkov and David Mazi\u00e8res. Kademlia: A peer-to-peer information system based on the xor metric. In Revised Papers from the First International Workshop on Peer-to-Peer Systems, IPTPS '01, page 53--65, Berlin, Heidelberg, 2002. Springer-Verlag."},{"key":"e_1_3_2_2_69_1","series-title":"Proceedings of Machine Learning Research","first-page":"1273","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273--1282. PMLR, 20-22 Apr 2017."},{"key":"e_1_3_2_2_70_1","volume-title":"Advances in Neural Information Processing Systems Workshop on Robust AI in Financial Services","author":"Mugunthan Vaikkunth","year":"2019","unstructured":"Vaikkunth Mugunthan, Antigoni Polychroniadou, David Byrd, and Tucker Hybinette Balch. Smpai: Secure multi-party computation for federated learning. In Advances in Neural Information Processing Systems Workshop on Robust AI in Financial Services, 2019."},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3085429"},{"key":"e_1_3_2_2_73_1","volume-title":"Luke Carlson, Filip Granqvist, Chris Vandevelde, et al. Federated evaluation and tuning for on-device personalization: System design & applications. arXiv preprint arXiv:2102.08503","author":"Paulik Matthias","year":"2021","unstructured":"Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al. Federated evaluation and tuning for on-device personalization: System design & applications. arXiv preprint arXiv:2102.08503, 2021."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975212"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2002.1019369"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/383059.383072"},{"key":"e_1_3_2_2_77_1","volume-title":"International Conference on Learning Representations","author":"Reddi Sashank J.","year":"2021","unstructured":"Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dn\u00fd, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, 2021."},{"key":"e_1_3_2_2_78_1","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics","volume":"108","author":"Reisizadeh Amirhossein","year":"2021","unstructured":"Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 2021--2031. PMLR, 26-28 Aug 2020."},{"key":"e_1_3_2_2_79_1","series-title":"Proceedings of Machine Learning Research","first-page":"8253","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Rothchild Daniel","year":"2020","unstructured":"Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora. FetchSGD: Communication-efficient federated learning with sketching. In Hal Daum\u00e9 III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 8253--8265. PMLR, 13-18 Jul 2020."},{"key":"e_1_3_2_2_80_1","first-page":"329","volume-title":"Proceedings of the IFIP\/ACM International Conference on Distributed Systems Platforms Heidelberg, Middleware '01","author":"Antony I.","year":"2001","unstructured":"Antony I. T. Rowstron and Peter Druschel. Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In Proceedings of the IFIP\/ACM International Conference on Distributed Systems Platforms Heidelberg, Middleware '01, page 329--350, Berlin, Heidelberg, 2001. Springer-Verlag."},{"key":"e_1_3_2_2_81_1","volume-title":"Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731","author":"Roy Abhijit Guha","year":"2019","unstructured":"Abhijit Guha Roy, Shayan Siddiqui, Sebastian P\u00f6lsterl, Nassir Navab, and Christian Wachinger. Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731, 2019."},{"key":"e_1_3_2_2_82_1","first-page":"11220","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Singhal Karan","year":"2021","unstructured":"Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 11220--11232. Curran Associates, Inc., 2021."},{"key":"e_1_3_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000068"},{"key":"e_1_3_2_2_84_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Smith Virginia","year":"2017","unstructured":"Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar. Federated multi-task learning. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017."},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/383059.383071"},{"key":"e_1_3_2_2_86_1","volume-title":"Semi-synchronous federated learning for energy-efficient training and accelerated convergence in cross-silo settings. ACM Transactions on Intelligent Systems and Technology, 13(5), jun","author":"Stripelis Dimitris","year":"2022","unstructured":"Dimitris Stripelis, Paul M. Thompson, and Jos\u00e9 Luis Ambite. Semi-synchronous federated learning for energy-efficient training and accelerated convergence in cross-silo settings. ACM Transactions on Intelligent Systems and Technology, 13(5), jun 2022."},{"key":"e_1_3_2_2_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2747409"},{"key":"e_1_3_2_2_88_1","series-title":"Proceedings of Machine Learning Research","first-page":"4848","volume-title":"Jennifer Dy and Andreas Krause","author":"Tang Hanlin","year":"2018","unstructured":"Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu. d2: Decentralized training over decentralized data. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 4848--4856. PMLR, 10-15 Jul 2018."},{"key":"e_1_3_2_2_89_1","volume-title":"Advances in Neural Information Processing Systems","author":"Teng Michael","year":"2018","unstructured":"Michael Teng and Frank Wood. Bayesian distributed stochastic gradient descent. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018."},{"key":"e_1_3_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.5555\/3388242.3388254"},{"key":"e_1_3_2_2_91_1","series-title":"Proceedings of Machine Learning Research","first-page":"509","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"Vanhaesebrouck Paul","year":"2017","unstructured":"Paul Vanhaesebrouck, Aur\u00e9lien Bellet, and Marc Tommasi. Decentralized Collaborative Learning of Personalized Models over Networks. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 509--517. PMLR, 20-22 Apr 2017."},{"key":"e_1_3_2_2_92_1","volume-title":"H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc","author":"Vogels Thijs","year":"2019","unstructured":"Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi. Powersgd: Practical low-rank gradient compression for distributed optimization. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019."},{"key":"e_1_3_2_2_93_1","volume-title":"Proceedings of Machine Learning and Systems","author":"Wang Ewen","year":"2023","unstructured":"Ewen Wang, Boyi Chen, Mosharaf Chowdhury, Ajay Kannan, and Franco Liang. Flint: A platform for federated learning integration. Proceedings of Machine Learning and Systems, 2023."},{"key":"e_1_3_2_2_94_1","first-page":"212","article-title":"Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd","volume":"1","author":"Wang Jianyu","year":"2019","unstructured":"Jianyu Wang and Gauri Joshi. Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd. Proceedings of Machine Learning and Systems, 1:212--229, 2019.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"e_1_3_2_2_96_1","volume-title":"Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209","author":"Warden Pete","year":"2018","unstructured":"Pete Warden. Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209, 2018."},{"key":"e_1_3_2_2_97_1","volume-title":"Advances in Neural Information Processing Systems","author":"Wen Zheng","year":"2017","unstructured":"Zheng Wen, Branislav Kveton, Michal Valko, and Sharan Vaswani. Online influence maximization under independent cascade model with semi-bandit feedback. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017."},{"key":"e_1_3_2_2_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3045266"},{"key":"e_1_3_2_2_99_1","volume-title":"Advances in Neural Information Processing Systems Workshop on Optimization for Machine Learning","author":"Xie Cong","year":"2020","unstructured":"Cong Xie, Sanmi Koyejo, and Indranil Gupta. Asynchronous federated optimization. In Advances in Neural Information Processing Systems Workshop on Optimization for Machine Learning, 2020."},{"key":"e_1_3_2_2_100_1","volume-title":"Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903","author":"Yang Timothy","year":"2018","unstructured":"Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Fran\u00e7oise Beaufays. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903, 2018."},{"key":"e_1_3_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2928176"},{"key":"e_1_3_2_2_102_1","doi-asserted-by":"crossref","unstructured":"Hao Yu Sen Yang and Shenghuo Zhu. Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence AAAI'19\/IAAI'19\/EAAI'19. AAAI Press 2019.","DOI":"10.1609\/aaai.v33i01.33015693"},{"key":"e_1_3_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230554"},{"key":"e_1_3_2_2_104_1","first-page":"493","volume-title":"2020 USENIX Annual Technical Conference (USENIX ATC 20)","author":"Zhang Chengliang","year":"2020","unstructured":"Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. BatchCrypt: Efficient homomorphic encryption for Cross-Silo federated learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20), pages 493--506. USENIX Association, July 2020."},{"key":"e_1_3_2_2_105_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488776"},{"key":"e_1_3_2_2_106_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533794"},{"key":"e_1_3_2_2_107_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2003.818784"},{"key":"e_1_3_2_2_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/2504730.2504752"}],"event":{"name":"EuroSys '24: Nineteenth European Conference on Computer Systems","location":"Athens Greece","acronym":"EuroSys '24","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the Nineteenth European Conference on Computer Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627703.3629575","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627703.3629575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:06:57Z","timestamp":1755824817000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627703.3629575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":108,"alternative-id":["10.1145\/3627703.3629575","10.1145\/3627703"],"URL":"https:\/\/doi.org\/10.1145\/3627703.3629575","relation":{},"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"2024-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}