mlsanityData to 7 Oct 2026

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

arXiv 2003.13376 ↗First posted 30 Mar 2020cs.CR cs.DC cs.LGcited by 64

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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.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 15; 2024: 16; 2025: 13; 2026: 1.

64 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  3. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
  4. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  5. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  6. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  7. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  8. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  9. 2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
  10. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  11. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  12. 2021Communication and Computation Reduction for Split Learning using Asynchronous Trainingcited by 11
38 more
  1. 2021Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learningcited by 10
  2. 2022Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutationcited by 10
  3. 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
  4. 2021Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregationcited by 8
  5. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  6. 2023Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attackscited by 8
  7. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  8. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  9. 2023On Feasibility of Server-side Backdoor Attacks on Split Learningcited by 6
  10. 2023On the Robustness of Split Learning against Adversarial Attackscited by 6
  11. 2023Split Without a Leak: Reducing Privacy Leakage in Split Learningcited by 6
  12. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  13. 2022LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Datacited by 5
  14. 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
  15. 2022AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networkscited by 4
  16. 2023TinyReptile: TinyML with Federated Meta-Learningcited by 4
  17. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  18. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splittingcited by 4
  19. 2021FedFly: Towards Migration in Edge-based Distributed Federated Learningcited by 3
  20. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  21. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
  22. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  23. 2024UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environmentscited by 3
  24. 2024Parallel Split Learning with Global Samplingcited by 3
  25. 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
  26. 2021Vulnerability Due to Training Order in Split Learningcited by 2
  27. 2022PiPar: Pipeline Parallelism for Collaborative Machine Learningcited by 2
  28. 2023TinyMetaFed: Efficient Federated Meta-Learning for TinyMLcited by 2
  29. 2023Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Taskscited by 2
  30. 2024Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systemscited by 2
  31. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  32. 2024Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMixcited by 2
  33. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  34. 2025Split Adaptation for Pre-trained Vision Transformerscited by 2
  35. 2025P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devicescited by 2
  36. 2025Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devicescited by 2
  37. 2025Data Heterogeneity and Forgotten Labels in Split Federated Learningcited by 2
  38. 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuumcited by 1

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