{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:33:25Z","timestamp":1772645605899,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"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":[[2021,12,7]]},"DOI":"10.1145\/3488659.3493775","type":"proceedings-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T23:40:00Z","timestamp":1638488400000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["FL_PyTorch"],"prefix":"10.1145","author":[{"given":"Konstantin","family":"Burlachenko","sequence":"first","affiliation":[{"name":"KAUST, Thuwal, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Horv\u00e1th","sequence":"additional","affiliation":[{"name":"KAUST, Thuwal, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Richt\u00e1rik","sequence":"additional","affiliation":[{"name":"KAUST, Thuwal, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294934"},{"key":"e_1_3_2_2_3_1","unstructured":"Apple. 2019. Designing for Privacy (video and slide deck). Apple WWDC https:\/\/developer.apple.com\/videos\/play\/wwdc2019\/708.  Apple. 2019. Designing for Privacy (video and slide deck). Apple WWDC https:\/\/developer.apple.com\/videos\/play\/wwdc2019\/708."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417885"},{"key":"e_1_3_2_2_5_1","volume-title":"Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390","author":"Beutel Daniel J","year":"2020"},{"key":"e_1_3_2_2_6_1","volume-title":"Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097","author":"Caldas Sebastian","year":"2018"},{"key":"e_1_3_2_2_7_1","volume-title":"Convergence and Accuracy Tradeoffs in Federated Learning and Meta-Learning. arXiv preprint arXiv:2103.05032","author":"Charles Zachary","year":"2021"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1791834.1791836"},{"key":"e_1_3_2_2_9_1","unstructured":"European Commission. [n. d.]. GDPR: 2018 Reform of EU Data Protection Rules. https:\/\/ec.europa.eu\/commission\/sites\/beta-political\/files\/data-protection-factsheet-changes_en.pdf  European Commission. [n. d.]. GDPR: 2018 Reform of EU Data Protection Rules. https:\/\/ec.europa.eu\/commission\/sites\/beta-political\/files\/data-protection-factsheet-changes_en.pdf"},{"key":"e_1_3_2_2_10_1","unstructured":"Google. 2021. Your voice and audio data stays private while Google Assistant improves. https:\/\/support.google.com\/assistant\/answer\/10176224.  Google. 2021. Your voice and audio data stays private while Google Assistant improves. https:\/\/support.google.com\/assistant\/answer\/10176224."},{"key":"e_1_3_2_2_11_1","volume-title":"MARINA: Faster Non-Convex Distributed Learning with Compression. arXiv preprint arXiv:2102.07845","author":"Gorbunov Eduard","year":"2021"},{"key":"e_1_3_2_2_12_1","volume-title":"Federated Learning for Mobile Keyboard Prediction. arXiv preprint arXiv:1811.03604","author":"Hard Andrew","year":"2018"},{"key":"e_1_3_2_2_13_1","volume-title":"FedML: A Research Library and Benchmark for Federated Machine Learning. arXiv preprint arXiv:2007.13518","author":"He Chaoyang","year":"2020"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_15_1","volume-title":"Marco Canini, and Peter Richtarik.","author":"Horvath Samuel","year":"2019"},{"key":"e_1_3_2_2_16_1","volume-title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. arXiv preprint arXiv:2102.13451","author":"Horvath Samuel","year":"2021"},{"key":"e_1_3_2_2_17_1","unstructured":"Alex Ingerman and Krzys Ostrowski. 2019. TensorFlow Federated. https:\/\/medium.com\/tensorflow\/introducing-tensorflow-federated-a4147aa20041  Alex Ingerman and Krzys Ostrowski. 2019. TensorFlow Federated. https:\/\/medium.com\/tensorflow\/introducing-tensorflow-federated-a4147aa20041"},{"key":"e_1_3_2_2_18_1","unstructured":"Intel and Consilient. 2020. Intel and Consilient Join Forces to Fight Financial Fraud with AI. https:\/\/newsroom.intel.com\/news\/intel-consilient-join-forces-fight-financial-fraud-ai\/.  Intel and Consilient. 2020. Intel and Consilient Join Forces to Fight Financial Fraud with AI. https:\/\/newsroom.intel.com\/news\/intel-consilient-join-forces-fight-financial-fraud-ai\/."},{"key":"e_1_3_2_2_19_1","unstructured":"Intel\u00ae. 2021. Intel\u00ae Open Federated Learning. https:\/\/github.com\/intel\/openfl  Intel\u00ae. 2021. Intel\u00ae Open Federated Learning. https:\/\/github.com\/intel\/openfl"},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning. PMLR, 5132--5143","author":"Karimireddy Sai Praneeth","year":"2020"},{"key":"e_1_3_2_2_21_1","volume-title":"Hamid Reza Feyzmahdavian, and Mikael Johansson","author":"Khirirat Sarit","year":"2018"},{"key":"e_1_3_2_2_22_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2018"},{"key":"e_1_3_2_2_23_1","first-page":"105","article-title":"PaddlePaddle: An Open-Source Deep Learning Platform from Industrial Practice","volume":"1","author":"Ma Yanjun","year":"2019","journal-title":"Frontiers of Data and Domputing"},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 1273--1282","author":"McMahan H Brendan","year":"2017"},{"key":"e_1_3_2_2_25_1","unstructured":"MELLODDY. 2020. MELLODDY Project Meets Its Year One Objective: Deployment Of The World's First Secure Platform For Multi-Task Federated Learning In Drug Discovery Among 10 Pharmaceutical Companies. https:\/\/www.melloddy.eu\/y1announcement.  MELLODDY. 2020. MELLODDY Project Meets Its Year One Objective: Deployment Of The World's First Secure Platform For Multi-Task Federated Learning In Drug Discovery Among 10 Pharmaceutical Companies. https:\/\/www.melloddy.eu\/y1announcement."},{"key":"e_1_3_2_2_26_1","volume-title":"Distributed Learning with Compressed Gradient Differences. CoRR, abs\/2019.09269","author":"Mishchenko K","year":"2019"},{"key":"e_1_3_2_2_27_1","unstructured":"NVIDIA. 2019. NVIDIA Clara. https:\/\/developer.nvidia.com\/clara  NVIDIA. 2019. NVIDIA Clara. https:\/\/developer.nvidia.com\/clara"},{"key":"e_1_3_2_2_28_1","unstructured":"NVIDIA. 2020. Triaging COVID-19 Patients: 20 Hospitals in 20 Days Build AI Model that Predicts Oxygen Needs. https:\/\/blogs.nvidia.com\/blog\/2020\/10\/05\/federated-learning-covid-oxygen-needs\/.  NVIDIA. 2020. Triaging COVID-19 Patients: 20 Hospitals in 20 Days Build AI Model that Predicts Oxygen Needs. https:\/\/blogs.nvidia.com\/blog\/2020\/10\/05\/federated-learning-covid-oxygen-needs\/."},{"key":"e_1_3_2_2_29_1","volume-title":"Story of the 1st Federated Learning Model at Owkin. https:\/\/owkin.com\/federated-learning\/federated-model\/"},{"key":"e_1_3_2_2_30_1","volume-title":"Paszke","author":"Adam","year":"2019"},{"key":"e_1_3_2_2_31_1","unstructured":"PyQT. [n.d.]. ([n.d.]).  PyQT. [n.d.]. ([n.d.])."},{"key":"e_1_3_2_2_32_1","volume-title":"Federated Learning for Emoji Prediction in a Mobile Keyboard. arXiv preprint arXiv:1906.04329","author":"Ramaswamy Swaroop","year":"2019"},{"key":"e_1_3_2_2_33_1","volume-title":"Adaptive federated optimization. arXiv preprint arXiv:2003.00295","author":"Reddi Sashank","year":"2020"},{"key":"e_1_3_2_2_34_1","volume-title":"A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017","author":"Ryffel Theo","year":"2018"},{"key":"e_1_3_2_2_35_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014"},{"key":"e_1_3_2_2_36_1","unstructured":"WeBank. 2020. Utilization of FATE in Anti Money Laundering Through Multiple Banks. https:\/\/www.fedai.org\/cases\/utilization-of-fate-in-anti-money-laundering-through-multiple-banks\/.  WeBank. 2020. Utilization of FATE in Anti Money Laundering Through Multiple Banks. https:\/\/www.fedai.org\/cases\/utilization-of-fate-in-anti-money-laundering-through-multiple-banks\/."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_2_38_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016"}],"event":{"name":"CoNEXT '21: The 17th International Conference on emerging Networking EXperiments and Technologies","location":"Virtual Event Germany","acronym":"CoNEXT '21","sponsor":["SIGCOMM ACM Special Interest Group on Data Communication"]},"container-title":["Proceedings of the 2nd ACM International Workshop on Distributed Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488659.3493775","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488659.3493775","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:12Z","timestamp":1750188672000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488659.3493775"}},"subtitle":["optimization research simulator for federated learning"],"short-title":[],"issued":{"date-parts":[[2021,12,7]]},"references-count":37,"alternative-id":["10.1145\/3488659.3493775","10.1145\/3488659"],"URL":"https:\/\/doi.org\/10.1145\/3488659.3493775","relation":{},"subject":[],"published":{"date-parts":[[2021,12,7]]},"assertion":[{"value":"2021-12-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}