# Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions

arXiv: https://arxiv.org/abs/2207.09611 · HTML: https://mlsanity.com/paper/2207.09611/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 112 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 30 of 71)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks](https://mlsanity.com/paper/1805.12076/) (2018)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [Continual Learning via Neural Pruning](https://mlsanity.com/paper/1903.04476/) (2019)
- [CRFL: Certifiably Robust Federated Learning against Backdoor Attacks](https://mlsanity.com/paper/2106.08283/) (2021)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)

## Built on by (most cited first, 14 of 14)

- [Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities](https://mlsanity.com/paper/2412.14538/) (2024)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning](https://mlsanity.com/paper/2311.09441/) (2023)
- [Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics](https://mlsanity.com/paper/2308.11027/) (2023)
- [Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks](https://mlsanity.com/paper/2307.03197/) (2023)
- [PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients](https://mlsanity.com/paper/2303.10624/) (2023)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication](https://mlsanity.com/paper/2405.07174/) (2024)
- [Federated Learning with Flexible Architectures](https://mlsanity.com/paper/2406.09877/) (2024)
- [A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networks](https://mlsanity.com/paper/2503.15559/) (2025)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)
- [Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization](https://mlsanity.com/paper/2602.15304/) (2026)
- [Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches](https://mlsanity.com/paper/2509.25555/) (2025)
- [StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting](https://mlsanity.com/paper/2605.26523/) (2026)

## Abstract (arXiv)

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with a hope to further arouse the research community's interest in this emerging field.