Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
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Abstract (arXiv)
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly sensitive location data. However, there are unique challenges involved with transitioning existing spatial temporal models to decentralized learning. In this survey paper, we review the existing literature that has proposed FL-based models for predicting human mobility, traffic prediction, community detection, location-based recommendation systems, and other spatial-temporal tasks. We describe the metrics and datasets these works have been using and create a baseline of these approaches in comparison to the centralized settings. Finally, we discuss the challenges of applying spatial-temporal models in a decentralized setting and by highlighting the gaps in the literature we provide a road map and opportunities for the research community.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 63 of its 213 references that are in the map. First posted on arXiv 9 May 2023.
Builds on
63 of 213 references.
References · 63
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
- 2020Adaptive Federated Optimizationcited by 1,046
- 2019Towards Federated Learning at Scale: System Designcited by 832
- 2018LEAF: A Benchmark for Federated Settingscited by 792
- 2020The Future of Digital Health with Federated Learningcited by 633
- 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
- 2020An Efficient Framework for Clustered Federated Learningcited by 511
- 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
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- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
- 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
- 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
- 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
- 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 185
- 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
- 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 172
- 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
- 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
- 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
- 2020Backdoor Attacks to Graph Neural Networkscited by 128
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 110
- 2020Federated Unsupervised Representation Learningcited by 93
- 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
- 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
- 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
- 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
- 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
- 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
- 2019A Troubling Analysis of Reproducibility and Progress in Recommender Systems Researchcited by 65
- 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
- 2020LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protectioncited by 57
- 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
- 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
- 2018Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchainscited by 49
- 2020Exploring Data Splitting Strategies for the Evaluation of Recommendation Modelscited by 48
- 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
- 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 35
- 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
- 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
- 2020Fair Active Learningcited by 30
- 2017Spatio-Temporal Data Mining: A Survey of Problems and Methodscited by 29
- 2020Dynamic Clustering in Federated Learningcited by 27
- 2023Interaction-level Membership Inference Attack Against Federated Recommender Systemscited by 25
- 2022Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attackscited by 21
- 2021Dual Attention-Based Federated Learning for Wireless Traffic Predictioncited by 18
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