## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
* _[PERSON] and [PERSON] (2024)_ [PERSON]; Yarivec-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach. _arXiv_ 2024, arXiv:2401.11836. [CrossRef]
* _[PERSON] and [PERSON] (2023)_ [PERSON]; [PERSON] [PERSON] FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks. _Multimed. Tools Appl._**2023**, _83_, 28891-28911. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON] [PERSON] Flexible Vertical Federated Learning with Heterogeneous Parties. _arXiv_ 2023, arXiv:2208.12672. [CrossRef] [PubMed]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data. _arXiv_ 2023, arXiv:2206.08330. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] SplitFed: When Federated Learning Meets Split Learning. _Proc. AAAI Conf. Artif. Intell._**2022**, _36_, 8485-8493. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Accelerating Federated Learning with Split Learning on Locally Generated Losses. In Proceedings of the ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality, Shanghai, China, 18-24 July 2021. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications. _Front. Comput. Sci._**2024**, _18_, 186356. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON], [PERSON] Utilizing Autoencoders to Improve Transfer Learning When Sensor Data Is Sparse. In Proceedings of the 9 th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Boston, MA, USA, 9-10 November 2022; ACM: New York, NY, USA, 2022; pp. 500-503. * _[PERSON] et al. (2002)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Autoencoder-Based Representation Learning and Its Application in Intelligent Fault Di-agnosis: A Review. _Measurement_**2022**, _189_, 110460. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Transfer Learning Based on Improved Stacked Autoencoder for Bearing Fault Diagnosis. _Knowl. Based Syst._**2022**, _256_, 109846. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A New Multilayer LSTM Method of Reconstruction for Compressed Sensing in Acquiring Human Pressure Data. In Proceedings of the 2017 11 th Asian Control Conference (ASCC), Gold Coast, Australia, 17-20 December 2017; IEEE: Piscataway, NJ, USA; pp. 2001-2006. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition. _IEEE Access_**2022**, _10_, 4137-4156. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]G. Universal Time-Series Representation Learn-ing: A Survey. _arXiv_**2024**, arXiv:2401.03717. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON] Transfer Learning in Environmental Remote Sensing. _Remote Sens. Environ._**2024**, _301_, 113924. [CrossRef]
* [PERSON] et al. (2018) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Transfer Learning for Time Series Classification. In Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 December 2018; IEEE: Piscataway, NJ, USA; pp. 1367-1376. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Survey on Time-Series Pre-Trained Models. _IEEE Trans. Knowl. Data Eng._**2024**, _36_, 7536-7555. [CrossRef]
* [PERSON] and [PERSON] (2024) [PERSON]; [PERSON] Diffusion-TS: Interpretable Diffusion for General Time Series Generation. _arXiv_**2024**, arXiv:2403.01742. * [PERSON] et al. (2017) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation.