# End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

arXiv: https://arxiv.org/abs/2003.13376 · HTML: https://mlsanity.com/paper/2003.13376/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 30 of 64)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning](https://mlsanity.com/paper/2107.04271/) (2021)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning](https://mlsanity.com/paper/2308.10279/) (2023)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [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)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Communication and Computation Reduction for Split Learning using Asynchronous Training](https://mlsanity.com/paper/2107.09786/) (2021)
- [Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning](https://mlsanity.com/paper/2107.05917/) (2021)
- [Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation](https://mlsanity.com/paper/2204.03500/) (2022)
- [Passive Inference Attacks on Split Learning via Adversarial Regularization](https://mlsanity.com/paper/2310.10483/) (2023)
- [Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation](https://mlsanity.com/paper/2106.00999/) (2021)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks](https://mlsanity.com/paper/2304.09515/) (2023)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)](https://mlsanity.com/paper/2501.06650/) (2025)
- [On Feasibility of Server-side Backdoor Attacks on Split Learning](https://mlsanity.com/paper/2302.09578/) (2023)
- [On the Robustness of Split Learning against Adversarial Attacks](https://mlsanity.com/paper/2307.07916/) (2023)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [An Efficient Privacy-aware Split Learning Framework for Satellite Communications](https://mlsanity.com/paper/2409.08538/) (2024)
- [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)
- [AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks](https://mlsanity.com/paper/2207.00415/) (2022)
- [TinyReptile: TinyML with Federated Meta-Learning](https://mlsanity.com/paper/2304.05201/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting](https://mlsanity.com/paper/2410.11577/) (2024)

## Abstract (arXiv)

This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution, but worse than FL under an extreme non-IID data distribution. For implementation overhead, we end-to-end mount both FL and SplitNN on Raspberry Pis, and comprehensively evaluate overheads including training time, communication overhead under the real LAN setting, power consumption and memory usage. Our key observations are that under IoT scenario where the communication traffic is the main concern, the FL appears to perform better over SplitNN because FL has the significantly lower communication overhead compared with SplitNN, which empirically corroborate previous statistical analysis. In addition, we reveal several unrecognized limitations about SplitNN, forming the basis for future research.