# FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2011.03180 · HTML: https://mlsanity.com/paper/2011.03180/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 23 of 23)

- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2301.00114/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://mlsanity.com/paper/2311.13348/) (2023)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data](https://mlsanity.com/paper/2203.04305/) (2022)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Federated Split GANs](https://mlsanity.com/paper/2207.01750/) (2022)
- [Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting](https://mlsanity.com/paper/2410.11577/) (2024)
- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)
- [MP-SL: Multihop Parallel Split Learning](https://mlsanity.com/paper/2402.00208/) (2024)
- [UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments](https://mlsanity.com/paper/2407.02693/) (2024)
- [Federated Split Learning with Improved Communication and Storage Efficiency](https://mlsanity.com/paper/2507.15816/) (2025)
- [Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training](https://mlsanity.com/paper/2405.13445/) (2024)
- [Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices](https://mlsanity.com/paper/2502.08692/) (2025)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)
- [CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning](https://mlsanity.com/paper/2511.18611/) (2025)
- [Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments](https://mlsanity.com/paper/2310.18479/) (2023)
- [Non-Federated Multi-Task Split Learning for Heterogeneous Sources](https://mlsanity.com/paper/2406.00150/) (2024)
- [Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks](https://mlsanity.com/paper/2410.12772/) (2024)
- [Traversal Learning: A Lossless And Efficient Distributed Learning Framework](https://mlsanity.com/paper/2504.07471/) (2025)

## Abstract (arXiv)

Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data distributed among clients without requiring direct access to their raw data. Existing FL and SL approaches work on horizontally or vertically partitioned data and cannot handle sequentially partitioned data where segments of multiple-segment sequential data are distributed across clients. In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train on sequential data are Recurrent Neural Networks (RNNs). Since the proposed framework is privacy-preserving, segments of multiple-segment sequential data cannot be shared between clients or between clients and server. To circumvent this limitation, we propose a novel SL approach tailored for RNNs. A RNN is split into sub-networks, and each sub-network is trained on one client containing single segments of multiple-segment training sequences. During local training, the sub-networks on different clients communicate with each other to capture latent dependencies between consecutive segments of multiple-segment sequential data on different clients, but without sharing raw data or complete model parameters. After training local sub-networks with local sequential data segments, all clients send their sub-networks to a federated server where sub-networks are aggregated to generate a global model. The experimental results on simulated and real-world datasets demonstrate that the proposed method successfully trains models on distributed sequential data, while preserving privacy, and outperforms previous FL and centralized learning approaches in terms of achieving higher accuracy in fewer communication rounds.