{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:47:47Z","timestamp":1777614467772,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1C1C1011063"],"award-info":[{"award-number":["2020R1C1C1011063"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information and Communications Technology Planning and Evaluation","award":["2020-0-01336"],"award-info":[{"award-number":["2020-0-01336"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539254","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"505-515","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Connecting Low-Loss Subspace for Personalized Federated Learning"],"prefix":"10.1145","author":[{"given":"Seok-Ju","family":"Hahn","sequence":"first","affiliation":[{"name":"Ulsan National Institute of Science and Technology &amp; Kakao Enterprise, Ulsan, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minwoo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Kakao Enterprise, Seongnam, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junghye","family":"Lee","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International Conference on Learning Representations.","author":"Emre Acar Durmus Alp","year":"2020","unstructured":"Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. 2020. Federated learning based on dynamic regularization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_2_1","volume-title":"Aaditya Kumar Singh, and Sunav Choudhary","author":"Arivazhagan Manoj Ghuhan","year":"2019","unstructured":"Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2885972"},{"key":"e_1_3_2_2_4_1","volume-title":"International Conference on Machine Learning. PMLR, 769--779","author":"Benton Gregory","year":"2021","unstructured":"Gregory Benton, Wesley Maddox, Sanae Lotfi, and Andrew Gordon Gordon Wilson. 2021. Loss surface simplexes for mode connecting volumes and fast ensembling. In International Conference on Machine Learning. PMLR, 769--779."},{"key":"e_1_3_2_2_5_1","volume-title":"International Conference on Machine Learning. PMLR, 1613--1622","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In International Conference on Machine Learning. PMLR, 1613--1622."},{"key":"e_1_3_2_2_6_1","volume-title":"Peter Wu, Tian Li, Jakub Kone?ny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar.","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Kone?ny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018)."},{"key":"e_1_3_2_2_7_1","volume-title":"G\u00e9rard Ben Arous, and Yann LeCun","author":"Choromanska Anna","year":"2015","unstructured":"Anna Choromanska, Mikael Henaff, Michael Mathieu, G\u00e9rard Ben Arous, and Yann LeCun. 2015. The loss surfaces of multilayer networks. In Artificial intelligence and statistics. PMLR, 192--204."},{"key":"e_1_3_2_2_8_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Collins Liam","year":"2021","unstructured":"Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021. Exploiting shared representations for personalized federated learning. In International Conference on Machine Learning. PMLR, 2089--2099."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_2_10_1","volume-title":"Mohammad Mahdi Kamani, and Mehrdad Mahdavi","author":"Deng Yuyang","year":"2020","unstructured":"Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi. 2020. Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461 (2020)."},{"key":"e_1_3_2_2_11_1","volume-title":"Personalized federated learning with moreau envelopes. arXiv preprint arXiv:2006.08848","author":"Dinh Canh T","year":"2020","unstructured":"Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen. 2020. Personalized federated learning with moreau envelopes. arXiv preprint arXiv:2006.08848 (2020)."},{"key":"e_1_3_2_2_12_1","volume-title":"FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization. arXiv preprint arXiv:2102.07148","author":"Dinh Canh T","year":"2021","unstructured":"Canh T Dinh, Tung T Vu, Nguyen H Tran, Minh N Dao, and Hongyu Zhang. 2021. FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization. arXiv preprint arXiv:2102.07148 (2021)."},{"key":"e_1_3_2_2_13_1","volume-title":"International conference on machine learning. PMLR, 1309--1318","author":"Draxler Felix","year":"2018","unstructured":"Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht. 2018. Essentially no barriers in neural network energy landscape. In International conference on machine learning. PMLR, 1309--1318."},{"key":"e_1_3_2_2_14_1","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah Alireza","year":"2020","unstructured":"Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neural Information Processing Systems 33 (2020), 3557-- 3568.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_15_1","volume-title":"Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757","author":"Fort Stanislav","year":"2019","unstructured":"Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757 (2019)."},{"key":"e_1_3_2_2_16_1","first-page":"6709","article-title":"Large scale structure of neural network loss landscapes","volume":"32","author":"Fort Stanislav","year":"2019","unstructured":"Stanislav Fort and Stanislaw Jastrzebski. 2019. Large scale structure of neural network loss landscapes. Advances in Neural Information Processing Systems 32 (2019), 6709--6717.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_17_1","volume-title":"arXiv preprint arXiv:2012.06898","author":"Frankle Jonathan","year":"2020","unstructured":"Jonathan Frankle. 2020. Revisiting\" Qualitatively Characterizing Neural Network Optimization Problems\". arXiv preprint arXiv:2012.06898 (2020)."},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Machine Learning. PMLR, 3259--3269","author":"Frankle Jonathan","year":"2020","unstructured":"Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. 2020. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning. PMLR, 3259--3269."},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems. 8803--8812","author":"Garipov Timur","year":"2018","unstructured":"Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Gordon Wilson. 2018. Loss surfaces, mode connectivity, and fast ensembling of dnns. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 8803--8812."},{"key":"e_1_3_2_2_20_1","volume-title":"An efficient framework for clustered federated learning. arXiv preprint arXiv:2006.04088","author":"Ghosh Avishek","year":"2020","unstructured":"Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020. An efficient framework for clustered federated learning. arXiv preprint arXiv:2006.04088 (2020)."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Machine Learning. PMLR, 1321--1330","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. In International Conference on Machine Learning. PMLR, 1321--1330."},{"key":"e_1_3_2_2_22_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31","author":"Han Bo","year":"2018","unstructured":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_2_23_1","volume-title":"Federated learning of a mixture of global and local models. arXiv preprint arXiv:2002.05516","author":"Hanzely Filip","year":"2020","unstructured":"Filip Hanzely and Peter Richt\u00e1rik. 2020. Federated learning of a mixture of global and local models. arXiv preprint arXiv:2002.05516 (2020)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_25_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_2_26_1","volume-title":"Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335","author":"Harry Hsu Tzu-Ming","year":"2019","unstructured":"Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335 (2019)."},{"key":"e_1_3_2_2_27_1","volume-title":"Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335","author":"Harry Hsu Tzu-Ming","year":"2019","unstructured":"Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335 (2019)."},{"key":"e_1_3_2_2_28_1","volume-title":"Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109","author":"Huang Gao","year":"2017","unstructured":"Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger. 2017. Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 (2017)."},{"key":"e_1_3_2_2_29_1","unstructured":"Pavel Izmailov Wesley J Maddox Polina Kirichenko Timur Garipov Dmitry Vetrov and Andrew Gordon Wilson. 2020. Subspace inference for Bayesian deep learning. In Uncertainty in Artificial Intelligence. PMLR 1169--1179."},{"key":"e_1_3_2_2_30_1","volume-title":"Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407","author":"Izmailov Pavel","year":"2018","unstructured":"Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018)."},{"key":"e_1_3_2_2_31_1","volume-title":"Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488","author":"Jiang Yihan","year":"2019","unstructured":"Yihan Jiang, Jakub Koneny, Keith Rush, and Sreeram Kannan. 2019. Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488 (2019)."},{"key":"e_1_3_2_2_32_1","volume-title":"SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR abs\/1910.06378","author":"Karimireddy Sai Praneeth","year":"2019","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. 2019. SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR abs\/1910.06378 (2019). arXiv:1910.06378 http:\/\/arxiv.org\/abs\/1910.06378"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00171"},{"key":"e_1_3_2_2_35_1","volume-title":"Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242","author":"Laine Samuli","year":"2016","unstructured":"Samuli Laine and Timo Aila. 2016. Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)."},{"key":"e_1_3_2_2_36_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint arXiv:1612.01474","author":"Lakshminarayanan Balaji","year":"2016","unstructured":"Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2016. Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint arXiv:1612.01474 (2016)."},{"key":"e_1_3_2_2_37_1","unstructured":"Ya Le and X. Yang. 2015. Tiny ImageNet Visual Recognition Challenge. Technical Report. Standford University."},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"6368","author":"Li Tian","year":"2021","unstructured":"Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021. Ditto: Fair and Robust Federated Learning Through Personalization. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 6357--6368. https:\/\/proceedings.mlr.press\/v139\/li21h.html"},{"key":"e_1_3_2_2_39_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2018","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/ pdf?id=6YEQUn0QICG","author":"Li Xiaoxiao","year":"2021","unstructured":"Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. 2021. Fed{BN}: Federated Learning on Non-{IID} Features via Local Batch Normalization. In International Conference on Learning Representations. https:\/\/openreview.net\/ pdf?id=6YEQUn0QICG"},{"key":"e_1_3_2_2_41_1","volume-title":"Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523","author":"Liang Paul Pu","year":"2020","unstructured":"Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)."},{"key":"e_1_3_2_2_42_1","first-page":"13153","article-title":"A simple baseline for bayesian uncertainty in deep learning","volume":"32","author":"Maddox Wesley J","year":"2019","unstructured":"Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. 2019. A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems 32 (2019), 13153-- 13164.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 (2020)."},{"key":"e_1_3_2_2_44_1","volume-title":"Federated Multi-Task Learning under a Mixture of Distributions. arXiv preprint arXiv:2108.10252","author":"Marfoq Othmane","year":"2021","unstructured":"Othmane Marfoq, Giovanni Neglia, Aur\u00e9lien Bellet, Laetitia Kameni, and Richard Vidal. 2021. Federated Multi-Task Learning under a Mixture of Distributions. arXiv preprint arXiv:2108.10252 (2021)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.03.002"},{"key":"e_1_3_2_2_46_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--1282."},{"key":"e_1_3_2_2_47_1","volume-title":"Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox.","author":"Nguyen Duc Tam","year":"2019","unstructured":"Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox. 2019. Self: Learning to filter noisy labels with self-ensembling. arXiv preprint arXiv:1910.01842 (2019)."},{"key":"e_1_3_2_2_48_1","volume-title":"Robust aggregation for federated learning. arXiv preprint arXiv:1912.13445","author":"Pillutla Krishna","year":"2019","unstructured":"Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. 2019. Robust aggregation for federated learning. arXiv preprint arXiv:1912.13445 (2019)."},{"key":"e_1_3_2_2_49_1","volume-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","author":"Sattler Felix","year":"2020","unstructured":"Felix Sattler, Klaus-Robert M\u00fcller, and Wojciech Samek. 2020. Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE transactions on neural networks and learning systems (2020)."},{"key":"e_1_3_2_2_50_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_2_51_1","volume-title":"Federated multi-task learning. arXiv preprint arXiv:1705.10467","author":"Smith Virginia","year":"2017","unstructured":"Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar. 2017. Federated multi-task learning. arXiv preprint arXiv:1705.10467 (2017)."},{"key":"e_1_3_2_2_52_1","volume-title":"Learning with symmetric label noise: The importance of being unhinged. Advances in neural information processing systems 28","author":"Rooyen Brendan Van","year":"2015","unstructured":"Brendan Van Rooyen, Aditya Menon, and Robert C Williamson. 2015. Learning with symmetric label noise: The importance of being unhinged. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_2_53_1","volume-title":"Federated Learning with Matched Averaging. In International Conference on Learning Representations. https:\/\/openreview.net\/forum? id=BkluqlSFDS","author":"Wang Hongyi","year":"2020","unstructured":"Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. 2020. Federated Learning with Matched Averaging. In International Conference on Learning Representations. https:\/\/openreview.net\/forum? id=BkluqlSFDS"},{"key":"e_1_3_2_2_54_1","volume-title":"Learning Neural Network Subspaces. arXiv preprint arXiv:2102.10472","author":"Wortsman Mitchell","year":"2021","unstructured":"Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, and Mohammad Rastegari. 2021. Learning Neural Network Subspaces. arXiv preprint arXiv:2102.10472 (2021)."},{"key":"e_1_3_2_2_55_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"7261","author":"Yurochkin Mikhail","year":"2019","unstructured":"Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian Nonparametric Federated Learning of Neural Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, California, USA, 7252--7261. http:\/\/proceedings.mlr.press\/v97\/yurochkin19a.html"},{"key":"e_1_3_2_2_56_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539254","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:59:58Z","timestamp":1750186798000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":55,"alternative-id":["10.1145\/3534678.3539254","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539254","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}