{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:05:42Z","timestamp":1775815542100,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100002418","name":"Intel Corporation","doi-asserted-by":"publisher","award":["UFunding 12679"],"award-info":[{"award-number":["UFunding 12679"]}],"id":[{"id":"10.13039\/100002418","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wuxi IoT Innovation Promotion Center","award":["2022SP-T13-C"],"award-info":[{"award-number":["2022SP-T13-C"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CRII-OAC-2153502"],"award-info":[{"award-number":["CRII-OAC-2153502"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2022YFB4402102"],"award-info":[{"award-number":["2022YFB4402102"]}]},{"name":"Eighth Research Institute in China Aerospace Science and Technology Corporation (Shanghai)","award":["USCAST2022-17"],"award-info":[{"award-number":["USCAST2022-17"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599345","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"3249-3261","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":86,"title":["FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4990-8466","authenticated-orcid":false,"given":"Jianqing","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5536-503X","authenticated-orcid":false,"given":"Yang","family":"Hua","sequence":"additional","affiliation":[{"name":"Queen's University Belfast, Belfast, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1444-2657","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Louisiana State University, Baton Rouge, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5965-3140","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7120-1622","authenticated-orcid":false,"given":"Zhengui","family":"Xue","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-8490","authenticated-orcid":false,"given":"Ruhui","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4714-7400","authenticated-orcid":false,"given":"Haibing","family":"Guan","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2594132"},{"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). 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","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E Hinton . 2016 . Layer Normalization . arXiv preprint arXiv:1607.06450 (2016). Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer Normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_2_4_1","volume-title":"On Bridging Generic and Personalized Federated Learning for Image Classification. In International Conference on Learning Representations (ICLR).","author":"Chen Hong-You","year":"2021","unstructured":"Hong-You Chen and Wei-Lun Chao . 2021 . On Bridging Generic and Personalized Federated Learning for Image Classification. In International Conference on Learning Representations (ICLR). Hong-You Chen and Wei-Lun Chao. 2021. On Bridging Generic and Personalized Federated Learning for Image Classification. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_5_1","volume-title":"A Downsampled Variant of Imagenet as an Alternative to the Cifar Datasets. arXiv preprint arXiv:1707.08819","author":"Chrabaszcz Patryk","year":"2017","unstructured":"Patryk Chrabaszcz , Ilya Loshchilov , and Frank Hutter . 2017. A Downsampled Variant of Imagenet as an Alternative to the Cifar Datasets. arXiv preprint arXiv:1707.08819 ( 2017 ). Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter. 2017. A Downsampled Variant of Imagenet as an Alternative to the Cifar Datasets. arXiv preprint arXiv:1707.08819 (2017)."},{"key":"e_1_3_2_2_6_1","volume-title":"Exploiting Shared Representations for Personalized Federated Learning. In International Conference on Machine Learning (ICML).","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 (ICML). Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021. Exploiting Shared Representations for Personalized Federated Learning. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_7_1","volume-title":"A guide to the california consumer privacy act of","author":"de la Torre Lydia","year":"2018","unstructured":"Lydia de la Torre . 2018. A guide to the california consumer privacy act of 2018 . Available at SSRN 3275571 (2018). Lydia de la Torre. 2018. A guide to the california consumer privacy act of 2018. Available at SSRN 3275571 (2018)."},{"key":"e_1_3_2_2_8_1","volume-title":"Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","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. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_9_1","volume-title":"International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Geiping Jonas","year":"2020","unstructured":"Jonas Geiping , Hartmut Bauermeister , Hannah Dr\u00f6ge , and Michael Moeller . 2020 . Inverting gradients-how easy is it to break privacy in federated learning? . In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Jonas Geiping, Hartmut Bauermeister, Hannah Dr\u00f6ge, and Michael Moeller. 2020. Inverting gradients-how easy is it to break privacy in federated learning?. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Gretton Arthur","year":"2006","unstructured":"Arthur Gretton , Karsten Borgwardt , Malte Rasch , Bernhard Sch\u00f6lkopf , and Alex Smola . 2006 . A Kernel Method for the Two-Sample-Problem . In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Sch\u00f6lkopf, and Alex Smola. 2006. A Kernel Method for the Two-Sample-Problem. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00494"},{"key":"e_1_3_2_2_12_1","volume-title":"Connecting Low-Loss Subspace for Personalized Federated Learning. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD).","author":"Hahn Seok-Ju","year":"2022","unstructured":"Seok-Ju Hahn , Minwoo Jeong , and Junghye Lee . 2022 . Connecting Low-Loss Subspace for Personalized Federated Learning. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Seok-Ju Hahn, Minwoo Jeong, and Junghye Lee. 2022. Connecting Low-Loss Subspace for Personalized Federated Learning. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)."},{"key":"e_1_3_2_2_13_1","volume-title":"Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_2_14_1","unstructured":"Geoffrey Hinton Oriol Vinyals Jeff Dean etal 2015. Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531 Vol. 2 7 (2015).  Geoffrey Hinton Oriol Vinyals Jeff Dean et al. 2015. Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531 Vol. 2 7 (2015)."},{"key":"e_1_3_2_2_15_1","volume-title":"Personalized Cross-Silo Federated Learning on Non-IID Data. In AAAI Conference on Artificial Intelligence (AAAI).","author":"Huang Yutao","year":"2021","unstructured":"Yutao Huang , Lingyang Chu , Zirui Zhou , Lanjun Wang , Jiangchuan Liu , Jian Pei , and Yong Zhang . 2021 . Personalized Cross-Silo Federated Learning on Non-IID Data. In AAAI Conference on Artificial Intelligence (AAAI). Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang. 2021. Personalized Cross-Silo Federated Learning on Non-IID Data. In AAAI Conference on Artificial Intelligence (AAAI)."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . In International Conference on Machine Learning (ICML). Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_17_1","volume-title":"Categorical Reparameterization with Gumbel-Softmax. arXiv preprint arXiv:1611.01144","author":"Jang Eric","year":"2016","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2016. Categorical Reparameterization with Gumbel-Softmax. arXiv preprint arXiv:1611.01144 ( 2016 ). Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical Reparameterization with Gumbel-Softmax. arXiv preprint arXiv:1611.01144 (2016)."},{"key":"e_1_3_2_2_18_1","volume-title":"Bag of Tricks for Efficient Text Classification. In Conference of the European Chapter of the Association for Computational Linguistics (EACL).","author":"Joulin Armand","year":"2017","unstructured":"Armand Joulin , Edouard Grave , Piotr Bojanowski , and Tomas Mikolov . 2017 . Bag of Tricks for Efficient Text Classification. In Conference of the European Chapter of the Association for Computational Linguistics (EACL). Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of Tricks for Efficient Text Classification. In Conference of the European Chapter of the Association for Computational Linguistics (EACL)."},{"key":"e_1_3_2_2_19_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz , H Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019 . Advances and Open Problems in Federated Learning . arXiv preprint arXiv:1912.04977 (2019). Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019. Advances and Open Problems in Federated Learning. arXiv preprint arXiv:1912.04977 (2019)."},{"key":"e_1_3_2_2_21_1","volume-title":"Nature","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015 . Deep Learning . Nature , Vol. 521 , 7553 (2015), 436--444. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep Learning. Nature, Vol. 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_23_1","volume-title":"Model-Contrastive Federated Learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Li Qinbin","year":"2021","unstructured":"Qinbin Li , Bingsheng He , and Dawn Song . 2021 a. Model-Contrastive Federated Learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Qinbin Li, Bingsheng He, and Dawn Song. 2021a. Model-Contrastive Federated Learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_2_24_1","volume-title":"Ditto: Fair and Robust Federated Learning Through Personalization. In International Conference on Machine Learning (ICML).","author":"Li Tian","year":"2021","unstructured":"Tian Li , Shengyuan Hu , Ahmad Beirami , and Virginia Smith . 2021 b. Ditto: Fair and Robust Federated Learning Through Personalization. In International Conference on Machine Learning (ICML). Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021b. Ditto: Fair and Robust Federated Learning Through Personalization. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_2_2_26_1","volume-title":"Federated Optimization in Heterogeneous Networks. In Conference on Machine Learning and Systems (MLSys).","author":"Li Tian","year":"2020","unstructured":"Tian Li , Anit Kumar Sahu , Manzil Zaheer , Maziar Sanjabi , Ameet Talwalkar , and Virginia Smith . 2020 b. Federated Optimization in Heterogeneous Networks. In Conference on Machine Learning and Systems (MLSys). Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020b. Federated Optimization in Heterogeneous Networks. In Conference on Machine Learning and Systems (MLSys)."},{"key":"e_1_3_2_2_27_1","volume-title":"FedPHP: Federated Personalization with Inherited Private Models. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML).","author":"Li Xin-Chun","year":"2021","unstructured":"Xin-Chun Li , De-Chuan Zhan , Yunfeng Shao , Bingshuai Li , and Shaoming Song . 2021 c. FedPHP: Federated Personalization with Inherited Private Models. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML). Xin-Chun Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, and Shaoming Song. 2021c. FedPHP: Federated Personalization with Inherited Private Models. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML)."},{"key":"e_1_3_2_2_28_1","volume-title":"Convergence Analysis of Two-Layer Neural Networks with Relu Activation. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Li Yuanzhi","year":"2017","unstructured":"Yuanzhi Li and Yang Yuan . 2017 . Convergence Analysis of Two-Layer Neural Networks with Relu Activation. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Yuanzhi Li and Yang Yuan. 2017. Convergence Analysis of Two-Layer Neural Networks with Relu Activation. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_29_1","volume-title":"Ensemble Distillation for Robust Model Fusion in Federated Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Lin Tao","year":"2020","unstructured":"Tao Lin , Lingjing Kong , Sebastian U Stich , and Martin Jaggi . 2020 . Ensemble Distillation for Robust Model Fusion in Federated Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020. Ensemble Distillation for Robust Model Fusion in Federated Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11630"},{"key":"e_1_3_2_2_31_1","volume-title":"International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Luo Mi","year":"2021","unstructured":"Mi Luo , Fei Chen , Dapeng Hu , Yifan Zhang , Jian Liang , and Jiashi Feng . 2021 . No fear of heterogeneity: Classifier calibration for federated learning with non-iid data . In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng. 2021. No fear of heterogeneity: Classifier calibration for federated learning with non-iid data. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Artificial Intelligence and Statistics (AISTATS).","author":"McMahan Brendan","year":"2017","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 International Conference on Artificial Intelligence and Statistics (AISTATS). Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In International Conference on Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_2_33_1","volume-title":"Machine learning: a probabilistic perspective","author":"Murphy Kevin P","unstructured":"Kevin P Murphy . 2012. Machine learning: a probabilistic perspective . MIT press . Kevin P Murphy. 2012. Machine learning: a probabilistic perspective. MIT press."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"e_1_3_2_2_35_1","volume-title":"Tadam: Task Dependent Adaptive Metric for Improved Few-Shot Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Oreshkin Boris","year":"2018","unstructured":"Boris Oreshkin , Pau Rodr\u00edguez L\u00f3pez , and Alexandre Lacoste . 2018 . Tadam: Task Dependent Adaptive Metric for Improved Few-Shot Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Boris Oreshkin, Pau Rodr\u00edguez L\u00f3pez, and Alexandre Lacoste. 2018. Tadam: Task Dependent Adaptive Metric for Improved Few-Shot Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_36_1","volume-title":"Improving the Fisher Kernel for Large-scale Image Classification. In European Conference on Computer Vision (ECCV).","author":"Perronnin Florent","year":"2010","unstructured":"Florent Perronnin , Jorge S\u00e1nchez , and Thomas Mensink . 2010 . Improving the Fisher Kernel for Large-scale Image Classification. In European Conference on Computer Vision (ECCV). Florent Perronnin, Jorge S\u00e1nchez, and Thomas Mensink. 2010. Improving the Fisher Kernel for Large-scale Image Classification. In European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Qin Can","year":"2019","unstructured":"Can Qin , Haoxuan You , Lichen Wang , C-C Jay Kuo , and Yun Fu . 2019 . Pointdan: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation . In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Can Qin, Haoxuan You, Lichen Wang, C-C Jay Kuo, and Yun Fu. 2019. Pointdan: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_38_1","unstructured":"Protection Regulation. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council. Regulation (eu) Vol. 679 (2016) 2016.  Protection Regulation. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council. Regulation (eu) Vol. 679 (2016) 2016."},{"key":"e_1_3_2_2_39_1","volume-title":"Balanced Meta-softmax for Long-tailed Visual Recognition. International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Ren Jiawei","year":"2020","unstructured":"Jiawei Ren , Cunjun Yu , Xiao Ma , Haiyu Zhao , Shuai Yi , 2020 . Balanced Meta-softmax for Long-tailed Visual Recognition. International Conference on Advances in Neural Information Processing Systems (NeurIPS). Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. 2020. Balanced Meta-softmax for Long-tailed Visual Recognition. International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Shamsian Aviv","year":"2021","unstructured":"Aviv Shamsian , Aviv Navon , Ethan Fetaya , and Gal Chechik . 2021 . Personalized Federated Learning using Hypernetworks . In International Conference on Machine Learning (ICML). Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. 2021. Personalized Federated Learning using Hypernetworks. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_42_1","volume-title":"Personalized Federated Learning with Moreau Envelopes. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Dinh Canh T","year":"2020","unstructured":"Canh T Dinh , Nguyen Tran , and Tuan Dung Nguyen . 2020 . Personalized Federated Learning with Moreau Envelopes. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Canh T Dinh, Nguyen Tran, and Tuan Dung Nguyen. 2020. Personalized Federated Learning with Moreau Envelopes. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_2_2_44_1","volume-title":"Convolutional Networks with Adaptive Inference Graphs. In European Conference on Computer Vision (ECCV).","author":"Veit Andreas","year":"2018","unstructured":"Andreas Veit and Serge Belongie . 2018 . Convolutional Networks with Adaptive Inference Graphs. In European Conference on Computer Vision (ECCV). Andreas Veit and Serge Belongie. 2018. Convolutional Networks with Adaptive Inference Graphs. In European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_2_45_1","volume-title":"Temporal Meta-Adaptor for Video Object Detection. In British Machine Vision Conference (BMVC).","author":"Wang Chi","year":"2021","unstructured":"Chi Wang , Yang Hua , Zheng Lu , Jian Gao , and Neil Robertson . 2021 . Temporal Meta-Adaptor for Video Object Detection. In British Machine Vision Conference (BMVC). Chi Wang, Yang Hua, Zheng Lu, Jian Gao, and Neil Robertson. 2021. Temporal Meta-Adaptor for Video Object Detection. In British Machine Vision Conference (BMVC)."},{"key":"e_1_3_2_2_46_1","volume-title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Wang Jianyu","unstructured":"Jianyu Wang , Qinghua Liu , Hao Liang , Gauri Joshi , and H. Vincent Poor . 2020 . Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. 2020. Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_47_1","volume-title":"Group Normalization. In European Conference on Computer Vision (ECCV).","author":"Wu Yuxin","year":"2018","unstructured":"Yuxin Wu and Kaiming He . 2018 . Group Normalization. In European Conference on Computer Vision (ECCV). Yuxin Wu and Kaiming He. 2018. Group Normalization. In European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_2_48_1","unstructured":"Yuezhou Wu Yan Kang Jiahuan Luo Yuanqin He Lixin Fan Rong Pan and Qiang Yang. 2022. FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).  Yuezhou Wu Yan Kang Jiahuan Luo Yuanqin He Lixin Fan Rong Pan and Qiang Yang. 2022. FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Liu Yang Ben Tan Vincent W Zheng Kai Chen and Qiang Yang. 2020. Federated Recommendation Systems. In Federated Learning. 225--239.  Liu Yang Ben Tan Vincent W Zheng Kai Chen and Qiang Yang. 2020. Federated Recommendation Systems. In Federated Learning. 225--239.","DOI":"10.1007\/978-3-030-63076-8_16"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_2_51_1","volume-title":"FedDisco: Federated Learning with Discrepancy-Aware Collaboration. arXiv preprint arXiv:2305.19229","author":"Ye Rui","year":"2023","unstructured":"Rui Ye , Mingkai Xu , Jianyu Wang , Chenxin Xu , Siheng Chen , and Yanfeng Wang . 2023. FedDisco: Federated Learning with Discrepancy-Aware Collaboration. arXiv preprint arXiv:2305.19229 ( 2023 ). Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu, Siheng Chen, and Yanfeng Wang. 2023. FedDisco: Federated Learning with Discrepancy-Aware Collaboration. arXiv preprint arXiv:2305.19229 (2023)."},{"key":"e_1_3_2_2_52_1","volume-title":"Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In International Conference on Machine Learning (ICML).","author":"Yu Lei","year":"2003","unstructured":"Lei Yu and Huan Liu . 2003 . Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In International Conference on Machine Learning (ICML). Lei Yu and Huan Liu. 2003. Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_53_1","volume-title":"2023 b. LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization. ACM Transactions on Information Systems","author":"Zhang Honglei","year":"2023","unstructured":"Honglei Zhang , Fangyuan Luo , Jun Wu , Xiangnan He , and Yidong Li . 2023 b. LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization. ACM Transactions on Information Systems ( 2023 ), 1--28. Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. 2023 b. LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization. ACM Transactions on Information Systems (2023), 1--28."},{"key":"e_1_3_2_2_54_1","volume-title":"FedALA: Adaptive Local Aggregation for Personalized Federated Learning. In AAAI Conference on Artificial Intelligence (AAAI).","author":"Zhang Jianqing","year":"2023","unstructured":"Jianqing Zhang , Yang Hua , Hao Wang , Tao Song , Zhengui Xue , Ruhui Ma , and Haibing Guan . 2023 a . FedALA: Adaptive Local Aggregation for Personalized Federated Learning. In AAAI Conference on Artificial Intelligence (AAAI). Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. 2023 a. FedALA: Adaptive Local Aggregation for Personalized Federated Learning. In AAAI Conference on Artificial Intelligence (AAAI)."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.015"},{"key":"e_1_3_2_2_56_1","volume-title":"Personalized Federated Learning with First Order Model Optimization. In International Conference on Learning Representations (ICLR).","author":"Zhang Michael","year":"2020","unstructured":"Michael Zhang , Karan Sapra , Sanja Fidler , Serena Yeung , and Jose M Alvarez . 2020 . Personalized Federated Learning with First Order Model Optimization. In International Conference on Learning Representations (ICLR). Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M Alvarez. 2020. Personalized Federated Learning with First Order Model Optimization. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_57_1","first-page":"1","article-title":"Deep learning based recommender system: A survey and new perspectives","volume":"52","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang , Lina Yao , Aixin Sun , and Yi Tay . 2019 . Deep learning based recommender system: A survey and new perspectives . ACM Computing Surverys , Vol. 52 , 1 (2019), 1 -- 38 . Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surverys, Vol. 52, 1 (2019), 1--38.","journal-title":"ACM Computing Surverys"},{"key":"e_1_3_2_2_58_1","volume-title":"Character-Level Convolutional Networks for Text Classification. In International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang , Junbo Zhao , and Yann LeCun . 2015 . Character-Level Convolutional Networks for Text Classification. In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-Level Convolutional Networks for Text Classification. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_59_1","volume-title":"International Conference on Advances in Neural Information Processing Systems (NeurIPS).","author":"Zhu Ligeng","year":"2019","unstructured":"Ligeng Zhu , Zhijian Liu , and Song Han . 2019 . Deep Leakage from Gradients . In International Conference on Advances in Neural Information Processing Systems (NeurIPS). Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep Leakage from Gradients. In International Conference on Advances in Neural Information Processing Systems (NeurIPS)."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599345","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599345","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:47Z","timestamp":1750178267000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":58,"alternative-id":["10.1145\/3580305.3599345","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599345","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}