mlsanityData to 7 Oct 2026

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

arXiv 2011.14818 ↗First posted 25 Nov 2020cs.LG cs.AI cs.CRcited by 34

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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.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 10; 2024: 9; 2025: 7.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  3. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
  4. 2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
  5. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  6. 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
  7. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  8. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  9. 2021Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performancecited by 10
  10. 2024SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimizationcited by 10
  11. 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
  12. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
22 more
  1. 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
  2. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  3. 2023On the Robustness of Split Learning against Adversarial Attackscited by 6
  4. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  5. 2023Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attackscited by 4
  6. 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flowscited by 4
  7. 2023Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computingcited by 3
  8. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  9. 2024Parallel Split Learning with Global Samplingcited by 3
  10. 2022PiPar: Pipeline Parallelism for Collaborative Machine Learningcited by 2
  11. 2024Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformationcited by 2
  12. 2025Split Adaptation for Pre-trained Vision Transformerscited by 2
  13. 2025P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devicescited by 2
  14. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  15. 2024Heterogeneous Federated Learning with Splited Language Modelcited by 1
  16. 2024Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classificationcited by 1
  17. 2025Oops!... They Stole it Again: Attacks on Split Learningcited by 1
  18. 2025Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architecturescited by 1
  19. 2023Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environmentscited by 0
  20. 2024Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devicescited by 0
  21. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  22. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0