{"type":"ScholarlyArticle","title":"Personalized Federated Learning for Cross-City Traffic Prediction","authors":[{"name":"Yu Zhang","slug":"yu-zhang","given":"Yu","family":"Zhang","affiliation":"School of Software , Shandong University (SDU) , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Hua Lu","slug":"hua-lu","given":"Hua","family":"Lu","affiliation":"Department of People and Technology , Roskilde University , Denmark"},{"name":"Ning Liu","slug":"ning-liu","given":"Ning","family":"Liu","affiliation":"School of Software , Shandong University (SDU) , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Yonghui Xu","slug":"yonghui-xu","given":"Yonghui","family":"Xu","affiliation":"School of Software , Shandong University (SDU) , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Qingzhong Li","slug":"qingzhong-li","given":"Qingzhong","family":"Li","affiliation":"School of Software , Shandong University (SDU) , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":"School of Software , Shandong University (SDU) , China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) , Shandong University , China"}],"abstract":"Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.","doi":"10.24963/ijcai.2024/611","url":"https://ijcai.org/proceedings/2024/611","pdf":"https://ijcai.org/proceedings/2024/0611.pdf","markdown":"https://ijcai.org/proceedings/2024/611.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/611","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":611,"pages":{"first":5526,"last":5534},"keywords":["Machine Learning: ML: Federated learning","Data Mining: DM: Mining spatial and/or temporal data","Multidisciplinary Topics and Applications: MTA: Transportation"],"videos":[],"references":[{"position":1,"raw":"Methods PEMS-BAY (Target city) METR-LA (Target city) MAE (↓) RMSE (↓) MAE (↓) RMSE (↓) 5 min 15 min 30 min 5 min 15 min 30 min 5 min 15 min 30 min 5 min 15 min","title":"Methods PEMS-BAY (Target city) METR-LA (Target city) MAE (↓) RMSE (↓) MAE (↓) RMSE","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Target-only baselines STGCN [Yu et al., 2017] 2.636 2.770 3.009 4.033 4.436 5.127 4.263 4.418 4.864 6.320 6.666 7.351 T-GCN [Zhao et al., 2019] 1.352 1.799 2.278 2.175 3.329 4.664 2.691 3.324 4.048 4.349 5.834 7.216 ST-Net (ours) 1.058 1.625 2.365 1.850 3.275 4.686 2.482 3.058 3.780 4.210 5.733 7.104 Cross-city baselines DastNet [Tang et al., 2022] 1.518 2.056 2.473 3.139 4.397 5.404 2.830 3.379 4.042 5.220 6.390 7.671 ST-GFSL [Lu et al., 2022] 1.158 1.637 2.169 1.981 3.261 4.638 2.464 3.106 3.816 4.141 5.614 6.928 Federated cross-city baselines FedAvg [McMahan et al., 2017] 2.030 2.741 3.144 2.864 4.111 5.305 3.234 3.679 4.430 5.243 6.427 7.691 FedProx [Li et al., 2020] 2.030 2.279 2.800 3.357 4.286 5.591 3.157 3.911 4.512 4.773 6.185 7.412 Per-FedAvg [Fallah et al., 2020] 2.305 2.646 3.084 4.121 4.965 5.910 3.022 3.572 4.214 4.894 6.189 7.439 pFedCTP (ours) 1.076 1.596 2.201 1.827 3.212 4.580 2.327 3.010 3.813 4.082 5.677 7.066 Methods Didi-Chengdu (Target city) Didi-Shenzhen (Target city) MAE (↓) RMSE (↓) MAE (↓) RMSE (↓) 10 min 30 min 60 min 10 min 30 min 60 min 10 min 30 min 60 min 10 min 30 min","title":"Methods Didi-Chengdu (Target city) Didi","authors":"Yu","year":"2017","venue":"Shenzhen (Target city) MAE (↓) RMSE (↓) MAE (↓) RMSE","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Target-only baselines STGCN [Yu et al., 2017] 2.930 2.905 3.093 4.249 4.248 4.491 2.740 2.738 2.873 3.858 3.877 4.087 T-GCN [Zhao et al., 2019] 2.340 2.850 3.350 3.369 4.136 4.845 2.109 2.589 3.010 3.177 3.920 4.572 ST-Net (ours) 2.327 2.768 3.135 3.256 3.994 4.570 1.932 2.355 2.675 2.804 3.551 4.124 Cross-city baselines DastNet [Tang et al., 2022] 2.874 3.230 3.739 4.145 4.661 5.346 2.335 2.651 3.110 3.508 4.018 4.704 ST-GFSL [Lu et al., 2022] 2.189 2.639 3.004 3.151 3.840 4.339 1.948 2.361 2.718 2.806 3.515 4.129 Federated cross-city baselines FedAvg [McMahan et al., 2017] 3.181 3.56 4.005 4.323 4.852 5.428 2.621 3.131 3.571 3.623 4.380 5.009 FedProx [Li et al., 2020] 3.086 3.645 4.088 3.940 4.777 5.400 2.473 2.904 3.290 3.290 4.003 4.613 Per-FedAvg [Fallah et al., 2020] 2.401 2.932 3.431 3.355 4.138 4.812 2.146 2.606 2.946 2.972 3.715 4.289 pFedCTP (ours) 2.144 2.661 3.003 3.099 3.896 4.394 1.889 2.339 2.699 2.746 3.522 4.123","title":"Target-only baselines STGCN","authors":"Yu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Arivazhagan et al., 2019] Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 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