# Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2310.10549 · HTML: https://mlsanity.com/paper/2310.10549/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2023.

## Built on by (most cited first, 11 of 11)

- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning](https://mlsanity.com/paper/2503.07869/) (2025)
- [RockNet: Distributed Learning on Ultra-Low-Power Devices](https://mlsanity.com/paper/2510.13320/) (2025)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [Personalized and Resilient Distributed Learning Through Opinion Dynamics](https://mlsanity.com/paper/2505.14081/) (2025)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)
- [Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance](https://mlsanity.com/paper/2610.14815/) (2026)

## Abstract (arXiv)

The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoT). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area.