{"type":"ScholarlyArticle","title":"Personalized Federated Learning With a Graph","authors":[{"name":"Fengwen Chen","slug":"fengwen-chen","given":"Fengwen","family":"Chen","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Zonghan Wu","slug":"zonghan-wu","given":"Zonghan","family":"Wu","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Tianyi Zhou","slug":"tianyi-zhou","given":"Tianyi","family":"Zhou","affiliation":"University of Washington , Seattle; University of Maryland , College Park"},{"name":"Jing Jiang","slug":"jing-jiang","given":"Jing","family":"Jiang","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"}],"abstract":"Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph. This paper aims to enhance the knowledge-sharing process in PFL by leveraging the graph-based structural information among clients. We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data. We cast SFL with graph into a novel optimization problem that can model the client-wise complex relations and graph-based structural topology by a unified framework. Moreover, in addition to using an existing relation graph, SFL could be expanded to learn the hidden relations among clients. Experiments on traffic and image benchmark datasets can demonstrate the effectiveness of the proposed method.","doi":"10.24963/ijcai.2022/357","url":"https://ijcai.org/proceedings/2022/357","pdf":"https://ijcai.org/proceedings/2022/0357.pdf","markdown":"https://ijcai.org/proceedings/2022/357.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/357","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":357,"pages":{"first":2575,"last":2582},"keywords":["Knowledge Representation and Reasoning: Reasong about actions","Data Mining: Federated Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986995","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985138","length_sec":521,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Arivazhagan et al., 2019] Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818, 2019.","title":"Federated learning with personalization layers","authors":"Vinay Manoj Ghuhan Arivazhagan, Aaditya Kumar Aggarwal, Sunav Singh, Choudhary","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Briggs et al., 2020] Christopher Briggs, Zhong Fan, and Pe- ter Andras. Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-9. IEEE, 2020.","title":"Federated learning with hierarchical clustering of local updates to improve training on non-iid data","authors":"Christopher Briggs, Zhong Fan, Peter Andras","year":"2020","venue":"2020 International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2018] Fei Chen, Mi Luo, Zhenhua Dong, Zhen- guo Li, and Xiuqiang He. Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876, 2018.","title":"Federated meta-learning with fast convergence and efficient communication","authors":"Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2019] Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, and Guodong Long. Dagcn: dual atten- tion graph convolutional networks. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2019.","title":"Dagcn: dual attention graph convolutional networks","authors":"Chen","year":"2019","venue":"2019 International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2021] Gary Cheng, Karan Chadha, and John Duchi. Fine-tuning is fine in federated learning. arXiv preprint arXiv:2108.07313, 2021.","title":"Fine-tuning is fine in federated learning","authors":"Gary Cheng, Karan Chadha, John Duchi","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deng et al., 2020] Yuyang Deng, Mohammad Mahdi Ka- mani, and Mehrdad Mahdavi. Adaptive personalized fed- erated learning. arXiv preprint arXiv:2003.13461, 2020.","title":"Adaptive personalized federated learning","authors":"Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fallah et al., 2020] Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neural Information Processing Systems, 33:3557-3568, 2020.","title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","authors":"Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ghosh et al., 2020] Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. An efficient framework for clustered federated learning. Advances in Neural In- formation Processing Systems, 33:19586-19597, 2020.","title":"An efficient framework for clustered federated learning","authors":"Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2021] Chaoyang He, Alay Dilipbhai Shah, Zhen- heng Tang, Di Fan1Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, and Salman Avestimehr. Fedcv: A federated learning framework for diverse computer vision tasks. arXiv preprint arXiv:2111.11066, 2021.","title":"Fedcv: A federated learning framework for diverse computer vision tasks","authors":"He","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jallepalli et al., 2021] Deepthi Jallepalli, Navya Chennagiri Ravikumar, Poojitha Vurtur Badarinath, Shravya Uchil, and Mahima Agumbe Suresh. Federated learning for ob- ject detection in autonomous vehicles. In 2021 IEEE Sev- enth International Conference on Big Data Computing Service and Applications (BigDataService), pages 107- 114. IEEE, 2021.","title":"Federated learning for object detection in autonomous vehicles","authors":"Deepthi Jallepalli, Navya Chennagiri Ravikumar, Poojitha Vurtur Badarinath, Shravya Uchil, Mahima Agumbe Suresh","year":"2021","venue":"IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ji et al., 2019] Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang. Learning private neural language modeling with attentive aggregation. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2019.","title":"Learning private neural language modeling with attentive aggregation","authors":"Ji","year":"2019","venue":"2019 International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jiang et al., 2020] Jing Jiang, Shaoxiong Ji, and Guodong Long. Decentralized knowledge acquisition for mobile in- ternet applications. World Wide Web, 23(5):2653-2669, 2020.","title":"Decentralized knowledge acquisition for mobile in-ternet applications","authors":"Jing Jiang, Shaoxiong Ji, Guodong Long","year":"2020","venue":"World Wide Web","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kairouz et al., 2021] Peter Kairouz, H Brendan McMa- han, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Ar- jun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2):1-210, 2021.","title":"Advances and open problems in federated learning","authors":"Peter Kairouz, Brendan Mcmahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Nitin Arjun, Kallista Bhagoji, Zachary Bonawitz, Graham Charles, Rachel Cormode, Cummings","year":"2021","venue":"Foundations and Trends® in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karimireddy et al., 2020] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebas- tian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020.","title":"Scaffold: Stochastic controlled averaging for federated learning","authors":"Praneeth Sai, Satyen Karimireddy, Mehryar Kale, Sashank Mohri, Sebastian Reddi, Ananda Stich, Suresh Theertha","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2018] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127, 2018.","title":"Federated optimization in heterogeneous networks","authors":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019a] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smithy. Feddane: A federated newton-type method. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pages 1227-1231. IEEE, 2019.","title":"Feddane: A federated newton-type method","authors":"Li","year":"2019","venue":"53rd Asilomar Conference on Signals, Systems, and Computers","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2019b] Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189, 2019.","title":"On the convergence of fedavg on non-iid data","authors":"Li","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2020] Tian Li, Anit Kumar Sahu, Ameet Tal- walkar, and Virginia Smith. Federated learning: Chal- lenges, methods, and future directions. IEEE Signal Pro- cessing Magazine, 37(3):50-60, 2020.","title":"Federated learning: Challenges, methods, and future directions","authors":"Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith","year":"2020","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2021] Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. Ditto: Fair and robust federated learn- ing through personalization. In International Conference on Machine Learning, pages 6357-6368. PMLR, 2021.","title":"Ditto: Fair and robust federated learning through personalization","authors":"Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liang et al., 2020] Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Rus- lan Salakhutdinov, and Louis-Philippe Morency. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523, 2020.","title":"Think locally, act globally: Federated learning with local and global representations","authors":"Paul Pu Liang, Terrance Liu, Liu Ziyin, Randy P Nicholas B Allen, David Auerbach, Ruslan Brent, Louis-Philippe Salakhutdinov, Morency","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Long et al., 2020] Guodong Long, Yue Tan, Jing Jiang, and Chengqi Zhang. Federated learning for open banking. In Federated learning, pages 240-254. Springer, 2020.","title":"Federated learning for open banking","authors":"Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang","year":"2020","venue":"Federated learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Long et al., 2022] Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, and Jing Jiang. Federated learn- ing for privacy-preserving open innovation future on dig- ital health. In Humanity Driven AI, pages 113-133. Springer, 2022.","title":"Federated learning for privacy-preserving open innovation future on digital health","authors":"Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, Jing Jiang","year":"2022","venue":"Humanity Driven AI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Luo et al., 2019] Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang. Real-world image datasets for federated learning. arXiv preprint arXiv:1910.11089, 2019.","title":"Real-world image datasets for federated learning","authors":"Luo","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ma et al., 2022] Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, and Chengqi Zhang. On the conver- gence of clustered federated learning. arXiv preprint arXiv:2202.06187, 2022.","title":"On the convergence of clustered federated learning","authors":"Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mansour et al., 2020] Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619, 2020.","title":"Three approaches for personalization with applications to federated learning","authors":"Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha, Suresh","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[McMahan et al., 2017] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Ar- cas. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, pages 1273-1282. PMLR, 2017.","title":"Communication-efficient learning of deep networks from decentralized data","authors":"Brendan Mcmahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera Y Arcas","year":"2017","venue":"Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Pan et al., 2016a] Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. Tri-party deep network representation. Network, 11(9):12, 2016.","title":"Tri-party deep network representation","authors":"Pan","year":"2016","venue":"Network","doi":null,"url":null,"internal_url":"/proceedings/2016/271"},{"position":29,"raw":"[Pan et al., 2016b] Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S Yuz. Joint structure feature exploration and regularization for multi-task graph classi- fication. In Data Engineering (ICDE), 2016 IEEE 32nd In- ternational Conference on, pages 1474-1475. IEEE, 2016.","title":"Joint structure feature exploration and regularization for multi-task graph classification","authors":"Pan","year":"2016","venue":"IEEE 32nd International Conference on","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Reddi et al., 2020] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMa- han. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.","title":"Adaptive federated optimization","authors":"Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, Brendan Mcmahan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rieke et al., 2020] Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyri- don Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al. The future of digital health with feder- ated learning. NPJ digital medicine, 3(1):1-7, 2020.","title":"The future of digital health with federated learning","authors":"Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Shadi Holger R Roth, Spyridon Albarqouni, Bakas, N Mathieu, Galtier, Klaus Bennett A Landman, Maier-Hein","year":"2020","venue":"NPJ digital medicine","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Shamsian et al., 2021] Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. Personalized federated learn- ing using hypernetworks. In International Conference on Machine Learning, pages 9489-9502. PMLR, 2021.","title":"Personalized federated learning using hypernetworks","authors":"Shamsian","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Shuman et al., 2013] David I Shuman, Sunil K Narang, Pas- cal Frossard, Antonio Ortega, and Pierre Vandergheynst. The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing maga- zine, 30(3):83-98, 2013.","title":"The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains","authors":"David I Shuman, K Sunil, Pascal Narang, Antonio Frossard, Pierre Ortega, Vandergheynst","year":"2013","venue":"IEEE signal processing magazine","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tan et al., 2021] Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. Fed- proto: Federated prototype learning over heterogeneous devices. arXiv preprint arXiv:2105.00243, 2021.","title":"Fedproto: Federated prototype learning over heterogeneous devices","authors":"Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tan et al., 2022] Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang. Toward personalized federated learning. IEEE Transactions on Neural Networks and Learning Sys- tems, 2022.","title":"Toward personalized federated learning","authors":"Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang","year":"2022","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2017a] Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. Mgae: Marginal- ized graph autoencoder for graph clustering. In Proceed- ings of the 2017 ACM on Conference on Information and Knowledge Management, pages 889-898, 2017.","title":"Mgae: Marginalized graph autoencoder for graph clustering","authors":"Wang","year":"2017","venue":"Proceed-ings of the 2017 ACM on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2017b] Zhitao Wang, Chengyao Chen, and Wenjie Li. Predictive network representation learning for link prediction. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pages 969-972, 2017.","title":"Predictive network representation learning for link prediction","authors":"Wang","year":"2017","venue":"Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2020] Binghui Wang, Ang Li, Hai Li, and Yi- ran Chen. Graphfl: A federated learning framework for semi-supervised node classification on graphs. arXiv preprint arXiv:2012.04187, 2020.","title":"Graphfl: A federated learning framework for semi-supervised node classification on graphs","authors":"Binghui Wang, Ang Li, Hai Li, Yiran Chen","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu et al., 2019] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121, 2019.","title":"Graph wavenet for deep spatial-temporal graph modeling","authors":"Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2019/264"},{"position":40,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A com- prehensive survey on graph neural networks. IEEE trans- actions on neural networks and learning systems, 32(1):4- 24, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xie et al., 2021] Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, and Chengqi Zhang. Multi-center federated learning. arXiv preprint arXiv:2108.08647, 2021.","title":"Multi-center federated learning","authors":"Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xu et al., 2021] Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang. Federated learn- ing for healthcare informatics. Journal of Healthcare In- formatics Research, 5(1):1-19, 2021.","title":"Federated learning for healthcare informatics","authors":"Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang","year":"2021","venue":"Journal of Healthcare Informatics Research","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2022] Yifei Zhang, Hao Zhu, Ziqiao Meng, Pi- otr Koniusz, and Irwin King. Graph-adaptive rectified lin- ear unit for graph neural networks. In Proceedings of the ACM Web Conference 2022, pages 1331-1339, 2022.","title":"Graph-adaptive rectified linear unit for graph neural networks","authors":"Yifei Zhang, Hao Zhu, Ziqiao Meng, Piotr Koniusz, Irwin King","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zheng et al., 2022] Zhaohua Zheng, Yize Zhou, Yilong Sun, Zhang Wang, Boyi Liu, and Keqiu Li. Applications of federated learning in smart cities: recent advances, taxon- omy, and open challenges. Connection Science, 34(1):1- 28, 2022.","title":"Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges","authors":"Zhaohua Zheng, Yize Zhou, Yilong Sun, Zhang Wang, Boyi Liu, Keqiu Li","year":"2022","venue":"Connection Science","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}