# Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

arXiv: https://arxiv.org/abs/2002.10671 · HTML: https://mlsanity.com/paper/2002.10671/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 30 of 59)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [A distillation-based approach integrating continual learning and federated learning for pervasive services](https://mlsanity.com/paper/2109.04197/) (2021)
- [CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning](https://mlsanity.com/paper/2204.03880/) (2022)
- [Multimodal Federated Learning on IoT Data](https://mlsanity.com/paper/2109.04833/) (2021)
- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels](https://mlsanity.com/paper/2207.06343/) (2022)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.12062/) (2021)
- [Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT](https://mlsanity.com/paper/2202.03575/) (2022)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Federated Few-Shot Learning with Adversarial Learning](https://mlsanity.com/paper/2104.00365/) (2021)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence](https://mlsanity.com/paper/2203.08176/) (2022)
- [Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks](https://mlsanity.com/paper/2303.10580/) (2023)
- [Turning Federated Learning Systems Into Covert Channels](https://mlsanity.com/paper/2104.10561/) (2021)
- [Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones](https://mlsanity.com/paper/2210.16918/) (2022)
- [Sparse Personalized Federated Learning](https://mlsanity.com/paper/2107.05330/) (2021)
- [Worldwide Federated Training of Language Models](https://mlsanity.com/paper/2405.14446/) (2024)
- [Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients](https://mlsanity.com/paper/2503.09206/) (2025)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Prompt-based Personalized Federated Learning for Medical Visual Question Answering](https://mlsanity.com/paper/2402.09677/) (2024)
- [Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence](https://mlsanity.com/paper/2303.12999/) (2023)

## Abstract (arXiv)

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneity in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.