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Intel\u00ae Software Guard Extensions. https:\/\/www.intel.com\/content\/www\/us\/en\/developer\/tools\/software-guard-extensions\/overview.html."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 308\u2013318","author":"Abadi Martin","year":"2016","unstructured":"Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. 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In Proceedings of the ACM on Web Conference 2025. 134\u2013146."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama Jo\u00e3o","year":"2014","unstructured":"Jo\u00e3o Gama, Indr\u0117 \u017dliobait\u0117, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM Computing Surveys (CSUR) 46, 4 (2014), 1\u201337.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_1_27_1","volume-title":"2019 IEEE international conference on big data (Big Data). IEEE, 2552\u20132559","author":"Gao Dashan","year":"2019","unstructured":"Dashan Gao, Yang Liu, Anbu Huang, Ce Ju, Han Yu, and Qiang Yang. 2019. Privacy-preserving heterogeneous federated transfer learning. In 2019 IEEE international conference on big data (Big Data). IEEE, 2552\u20132559."},{"key":"e_1_3_2_1_28_1","volume-title":"Apache kafka","author":"Garg Nishant","unstructured":"Nishant Garg. 2013. Apache kafka. Packt Publishing."},{"key":"e_1_3_2_1_29_1","volume-title":"Inverting Gradients-How easy is it to break privacy in federated learning? arXiv preprint arXiv:2003.14053","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? arXiv preprint arXiv:2003.14053 (2020)."},{"key":"e_1_3_2_1_30_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_1_31_1","unstructured":"Ozgu Goksu and Nicolas Pugeault. 2024. Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation. arXiv:2412.15010 [cs.LG] https:\/\/arxiv.org\/abs\/2412.15010"},{"key":"e_1_3_2_1_32_1","first-page":"723","article-title":"A Kernel Two-Sample Test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A Kernel Two-Sample Test. Journal of Machine Learning Research 13, 25 (2012), 723\u2013773. http:\/\/jmlr.org\/papers\/v13\/gretton12a.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_33_1","volume-title":"Web and Big Data: 5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, August 23\u201325, 2021, Proceedings, Part I 5. Springer, 480\u2013486","author":"Guo Binbin","year":"2021","unstructured":"Binbin Guo, Yuan Mei, Danyang Xiao, and Weigang Wu. 2021. PFL-MoE: Personalized federated learning based on mixture of experts. In Web and Big Data: 5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, August 23\u201325, 2021, Proceedings, Part I 5. Springer, 480\u2013486."},{"key":"e_1_3_2_1_34_1","volume-title":"FedconCeptEM: Robust federated learning under diverse distribution shifts. arXiv preprint arXiv:2301.12379","author":"Guo Yongxin","year":"2023","unstructured":"Yongxin Guo, Xiaoying Tang, and Tao Lin. 2023. FedconCeptEM: Robust federated learning under diverse distribution shifts. arXiv preprint arXiv:2301.12379 (2023)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Parisa Hamedi Roozbeh Razavi-Far and Ehsan Hallaji. 2025. Federated Continual Learning: Concepts Challenges and Solutions. arXiv:2502.07059 [cs.LG] https:\/\/arxiv.org\/abs\/2502.07059","DOI":"10.1016\/j.neucom.2025.130844"},{"key":"e_1_3_2_1_36_1","volume-title":"Mir Sazzat Hossain, A. K. M. Mahbubur Rahman, Sajib Mistry, M Ashraful Amin, and Amin Ahsan Ali.","author":"Rajib Rakibul Hasan","year":"2025","unstructured":"Rakibul Hasan Rajib, Md Akil Raihan Iftee, Mir Sazzat Hossain, A. K. M. Mahbubur Rahman, Sajib Mistry, M Ashraful Amin, and Amin Ahsan Ali. 2025. FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning. arXiv preprint arXiv:2505.13643 (2025)."},{"key":"e_1_3_2_1_37_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770\u2013778","author":"He K.","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770\u2013778."},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proceedings of the International Conference on Learning Representations (2019)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Ryan Hildebrant Rahul Bhope Sharad Mehrotra Christopher Tull and Nalini Venkatasubramanian. 2025. DIM-SUM: Dynamic IMputation for Smart Utility Management. arXiv:2506.20023 [cs.LG] https:\/\/arxiv.org\/abs\/2506.20023","DOI":"10.14778\/3749646.3749705"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523273"},{"key":"e_1_3_2_1_41_1","volume-title":"Weinberger","author":"Huang Gao","year":"2018","unstructured":"Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2018. Densely Connected Convolutional Networks. arXiv:1608.06993 [cs.CV]"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 10143\u201310153","author":"Huang Wenke","year":"2022","unstructured":"Wenke Huang, Mang Ye, and Bo Du. 2022. Learn from others and be yourself in heterogeneous federated learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 10143\u201310153."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"e_1_3_2_1_44_1","volume-title":"Test-Time Robust Personalization for Federated Learning. In International Conference on Learning Representations (ICLR).","author":"Jiang Le","year":"2023","unstructured":"Le Jiang and ... Lin. 2023. Test-Time Robust Personalization for Federated Learning. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_45_1","volume-title":"Gibbons","author":"Jothimurugesan Ellango","year":"2023","unstructured":"Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, and Phillip B. Gibbons. 2023. Federated Learning under Distributed Concept Drift. arXiv:2206.00799 [cs.LG] https:\/\/arxiv.org\/abs\/2206.00799"},{"key":"e_1_3_2_1_46_1","volume-title":"Keith 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, Keith 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_1_47_1","unstructured":"Solomon Kullback. 1951. Kullback-leibler divergence."},{"key":"e_1_3_2_1_48_1","volume-title":"USENIX Symposium on Operating Systems Design and Implementation (OSDI).","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. Efficient Federated Learning via Guided Participant Selection. In USENIX Symposium on Operating Systems Design and Implementation (OSDI)."},{"key":"e_1_3_2_1_49_1","volume-title":"Proc. IEEE 86 (12","author":"Lecun Yann","year":"1998","unstructured":"Yann Lecun, Leon Bottou, Y. Bengio, and Patrick Haffner. 1998. Gradient-Based Learning Applied to Document Recognition. Proc. IEEE 86 (12 1998), 2278\u20132324. 10.1109\/5.726791"},{"key":"e_1_3_2_1_50_1","unstructured":"Minghao Li Dmitrii Avdiukhin Rana Shahout Nikita Ivkin Vladimir Braverman and Minlan Yu. 2024. Federated Learning Clients Clustering with Adaptation to Data Drifts. arXiv:2411.01580 [cs.LG] https:\/\/arxiv.org\/abs\/2411.01580"},{"key":"e_1_3_2_1_51_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. In MLSys, I. Dhillon, D. Papailiopoulos, and V. Sze (Eds.). 429\u2013450."},{"key":"e_1_3_2_1_52_1","volume-title":"Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623","author":"Li Xiaoxiao","year":"2021","unstructured":"Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. 2021. Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)."},{"key":"e_1_3_2_1_53_1","volume-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 995\u20131005","author":"Li Xin-Chun","year":"2021","unstructured":"Xin-Chun Li and De-Chuan Zhan. 2021. Fedrs: Federated learning with restricted softmax for label distribution non-iid data. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 995\u20131005."},{"key":"e_1_3_2_1_54_1","unstructured":"Lifeng Liu Fengda Zhang Jun Xiao and Chao Wu. 2020. Evaluation Framework For Large-scale Federated Learning. arXiv:2003.01575 [cs.LG] https:\/\/arxiv.org\/abs\/2003.01575"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Christian Lohrmann et al. 2015. Elastic stream processing with latency guarantees. In IEEE ICDE.","DOI":"10.1109\/ICDCS.2015.48"},{"key":"e_1_3_2_1_56_1","first-page":"2346","article-title":"Learning under concept drift: A review","volume":"31","author":"Lu Jie","year":"2018","unstructured":"Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Jo\u00e3o Gama, and Guangquan Zhang. 2018. Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering 31, 12 (2018), 2346\u20132363.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_1_57_1","unstructured":"Othmane Marfoq Giovanni Neglia Laetitia Kameni and Richard Vidal. 2023. Federated Learning for Data Streams. arXiv:2301.01542 [cs.LG] https:\/\/arxiv.org\/abs\/2301.01542"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10462-012-9338-y","article-title":"Mixture of experts: a literature survey","volume":"42","author":"Masoudnia Saeed","year":"2014","unstructured":"Saeed Masoudnia and Reza Ebrahimpour. 2014. Mixture of experts: a literature survey. Artificial Intelligence Review 42 (2014), 275\u2013293.","journal-title":"Artificial Intelligence Review"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119235"},{"key":"e_1_3_2_1_60_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. 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Federated Learning under Covariate Shifts with Generalization Guarantees. arXiv:2306.05325 [cs.LG] https:\/\/arxiv.org\/abs\/2306.05325"},{"key":"e_1_3_2_1_69_1","volume-title":"Federated mixture of experts. arXiv preprint arXiv:2107.06724","author":"Reisser Matthias","year":"2021","unstructured":"Matthias Reisser, Christos Louizos, Efstratios Gavves, and Max Welling. 2021. Federated mixture of experts. arXiv preprint arXiv:2107.06724 (2021)."},{"key":"e_1_3_2_1_70_1","volume-title":"Interspeech","author":"Seide Frank","year":"2014","unstructured":"Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu. 2014. 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs. 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PMLR, 37860\u201337879."},{"key":"e_1_3_2_1_76_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747 [cs.LG] https:\/\/arxiv.org\/abs\/1708.07747"},{"key":"e_1_3_2_1_77_1","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management.","author":"Xu Jian","year":"2023","unstructured":"Jian Xu, Hongcheng Lin, Yifan Xu, Yingxue Li, Neil Zhenqiang Gong, and Meng Wang. 2023. A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management."},{"key":"e_1_3_2_1_78_1","unstructured":"Yaqian Xu Wenquan Cui Jianjun Xu and Haoyang Cheng. 2023. Federated Covariate Shift Adaptation for Missing Target Output Values. arXiv:2302.14427 [stat.ML] https:\/\/arxiv.org\/abs\/2302.14427"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3363240"},{"key":"e_1_3_2_1_80_1","volume-title":"Two-stream federated learning: Reduce the communication costs. In 2018 IEEE Visual Communications and Image Processing (VCIP)","author":"Yao Xin","unstructured":"Xin Yao, Chaofeng Huang, and Lifeng Sun. 2018. Two-stream federated learning: Reduce the communication costs. In 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 1\u20134."},{"key":"e_1_3_2_1_81_1","volume-title":"pFedMoE: Data-level personalization with mixture of experts for model-heterogeneous personalized federated learning. arXiv preprint arXiv:2402.01350","author":"Yi Liping","year":"2024","unstructured":"Liping Yi, Han Yu, Chao Ren, Heng Zhang, Gang Wang, Xiaoguang Liu, and Xiaoxiao Li. 2024. pFedMoE: Data-level personalization with mixture of experts for model-heterogeneous personalized federated learning. arXiv preprint arXiv:2402.01350 (2024)."},{"key":"e_1_3_2_1_82_1","volume-title":"See through Gradients: Image Batch Recovery via GradInversion. arXiv preprint arXiv:2104.07586","author":"Yin Hongxu","year":"2021","unstructured":"Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov. 2021. See through Gradients: Image Batch Recovery via GradInversion. arXiv preprint arXiv:2104.07586 (2021)."},{"key":"e_1_3_2_1_83_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"12086","author":"Yoon Jaehong","year":"2021","unstructured":"Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, and Sung Ju Hwang. 2021. Federated Continual Learning with Weighted Inter-client Transfer. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139). PMLR, 12073\u201312086. https:\/\/proceedings.mlr.press\/v139\/yoon21b.html"},{"key":"e_1_3_2_1_84_1","volume-title":"International Conference on Learning Representations.","author":"Yoon Tehrim","year":"2021","unstructured":"Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang. 2021. FedMix: Approximation of mixup under mean augmented federated learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_85_1","volume-title":"Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 423\u2013438","author":"Zaharia Matei","year":"2013","unstructured":"Matei Zaharia, Tathagata Das, Haoyuan Li, Timothy Hunter, Scott Shenker, and Ion Stoica. 2013. Discretized streams: Fault-tolerant streaming computation at scale. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. 423\u2013438."},{"key":"e_1_3_2_1_86_1","volume-title":"Overcoming label shift in targeted federated learning. arXiv preprint arXiv:2411.03799","author":"Zec Edvin Listo","year":"2024","unstructured":"Edvin Listo Zec, Adam Breitholtz, and Fredrik D Johansson. 2024. Overcoming label shift in targeted federated learning. arXiv preprint arXiv:2411.03799 (2024)."},{"key":"e_1_3_2_1_87_1","volume-title":"Leon Ren\u00e9 S\u00fctfeld, and Daniel Gillblad","author":"Zec Edvin Listo","year":"2020","unstructured":"Edvin Listo Zec, John Martinsson, Olof Mogren, Leon Ren\u00e9 S\u00fctfeld, and Daniel Gillblad. 2020. Federated learning using mixture of experts. (2020)."},{"key":"e_1_3_2_1_88_1","volume-title":"Leon Ren\u00e9 S\u00fctfeld, and Daniel Gillblad","author":"Zec Edvin Listo","year":"2021","unstructured":"Edvin Listo Zec, Olof Mogren, John Martinsson, Leon Ren\u00e9 S\u00fctfeld, and Daniel Gillblad. 2021. Specialized federated learning using a mixture of experts. arXiv:2010.02056 [cs.LG] https:\/\/arxiv.org\/abs\/2010.02056"},{"key":"e_1_3_2_1_89_1","volume-title":"Konda Reddy Mopuri, and Hakan Bilen","author":"Zhao Bo","year":"2020","unstructured":"Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. iDLG: Improved Deep Leakage from Gradients. arXiv preprint arXiv:2001.02610 (2020)."},{"key":"e_1_3_2_1_90_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)."},{"key":"e_1_3_2_1_91_1","volume-title":"Drift Detection and Adaptation for Federated Learning in IoT with Adaptive Device Management. In 2024 IEEE International Conference on Big Data (Big Data). 8088\u20138097","author":"Zhou Shuang","year":"2024","unstructured":"Shuang Zhou, Shashank Shekhar, Ajay Chhokra, Abhishek Dubey, and Aniruddha Gokhale. 2024. Drift Detection and Adaptation for Federated Learning in IoT with Adaptive Device Management. In 2024 IEEE International Conference on Big Data (Big Data). 8088\u20138097. 10.1109\/BigData62323.2024.110826139"},{"key":"e_1_3_2_1_92_1","volume-title":"International Conference on Learning Representations.","author":"Zhu Chen","year":"2021","unstructured":"Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Kone\u010dn\u1ef3, Andrew Hard, and Tom Goldstein. 2021. Diurnal or nocturnal? federated learning of multi-branch networks from periodically shifting distributions. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_93_1","volume-title":"FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting. npj Digital Medicine 8, 1","author":"Zhu He","year":"2025","unstructured":"He Zhu, Jun Bai, Na Li, Xiaoxiao Li, Dianbo Liu, David L Buckeridge, and Yue Li. 2025. FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting. npj Digital Medicine 8, 1 (2025), 1\u201319."},{"key":"e_1_3_2_1_94_1","unstructured":"Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep leakage from gradients. In Advances in Neural Information Processing Systems. 14774\u201314784."},{"key":"e_1_3_2_1_95_1","volume-title":"Pysyft: A library for easy federated learning. 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