# Advancements of federated learning towards privacy preservation: from federated learning to split learning

arXiv: https://arxiv.org/abs/2011.14818 · HTML: https://mlsanity.com/paper/2011.14818/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 30 of 34)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [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)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://mlsanity.com/paper/2311.13348/) (2023)
- [Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance](https://mlsanity.com/paper/2109.09246/) (2021)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization](https://mlsanity.com/paper/2410.10759/) (2024)
- [Passive Inference Attacks on Split Learning via Adversarial Regularization](https://mlsanity.com/paper/2310.10483/) (2023)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023)
- [SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)](https://mlsanity.com/paper/2501.06650/) (2025)
- [On the Robustness of Split Learning against Adversarial Attacks](https://mlsanity.com/paper/2307.07916/) (2023)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks](https://mlsanity.com/paper/2307.03197/) (2023)
- [All Rivers Run to the Sea: Private Learning with Asymmetric Flows](https://mlsanity.com/paper/2312.05264/) (2023)
- [Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing](https://mlsanity.com/paper/2310.13384/) (2023)
- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)
- [Parallel Split Learning with Global Sampling](https://mlsanity.com/paper/2407.15738/) (2024)
- [PiPar: Pipeline Parallelism for Collaborative Machine Learning](https://mlsanity.com/paper/2302.12803/) (2022)
- [Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation](https://mlsanity.com/paper/2410.09125/) (2024)
- [Split Adaptation for Pre-trained Vision Transformers](https://mlsanity.com/paper/2503.00441/) (2025)
- [P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices](https://mlsanity.com/paper/2507.17228/) (2025)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architectures](https://mlsanity.com/paper/2509.03695/) (2025)
- [Heterogeneous Federated Learning with Splited Language Model](https://mlsanity.com/paper/2403.16050/) (2024)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)

## Abstract (arXiv)

In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the latest innovations such as industry 4.0 and smart vehicles. FL provides privacy-by-design. It trains a machine learning model collaboratively over several distributed clients (ranging from two to millions) such as mobile phones, without sharing their raw data with any other participant. In practical scenarios, all clients do not have sufficient computing resources (e.g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e.g., rival parties). In this regard, FL is not sufficient, so split learning (SL) is introduced. SL is reliable in these scenarios as it splits a model into multiple portions, distributes them among clients and server, and trains/tests their respective model portions to accomplish the full model training/testing. In SL, the participants do not share both data and their model portions to any other parties, and usually, a smaller network portion is assigned to the clients where data resides. Recently, a hybrid of FL and SL, called splitfed learning, is introduced to elevate the benefits of both FL (faster training/testing time) and SL (model split and training). Following the developments from FL to SL, and considering the importance of SL, this chapter is designed to provide extensive coverage in SL and its variants. The coverage includes fundamentals, existing findings, integration with privacy measures such as differential privacy, open problems, and code implementation.