# Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2111.07494 · HTML: https://mlsanity.com/paper/2111.07494/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://mlsanity.com/paper/2110.14032/) (2021)
- [TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning](https://mlsanity.com/paper/2007.11622/) (2020)
- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [Improving on-device speaker verification using federated learning with privacy](https://mlsanity.com/paper/2008.02651/) (2020)
- [A Federated Learning Framework for Healthcare IoT devices](https://mlsanity.com/paper/2005.05083/) (2020)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge](https://mlsanity.com/paper/2007.14513/) (2020)
- [Deep Learning in the Era of Edge Computing: Challenges and Opportunities](https://mlsanity.com/paper/2010.08861/) (2020)
- [Secure Aggregation for Buffered Asynchronous Federated Learning](https://mlsanity.com/paper/2110.02177/) (2021)
- [Mobility-Aware Smart Charging of Electric Bus Fleets](https://mlsanity.com/paper/1912.05681/) (2019)

## Built on by (most cited first, 30 of 50)

- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [FedMultimodal: A Benchmark For Multimodal Federated Learning](https://mlsanity.com/paper/2306.09486/) (2023)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training](https://mlsanity.com/paper/2304.06947/) (2023)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [GPT-FL: Generative Pre-trained Model-Assisted Federated Learning](https://mlsanity.com/paper/2306.02210/) (2023)
- [GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning](https://mlsanity.com/paper/2308.10279/) (2023)
- [NoT: Federated Unlearning via Weight Negation](https://mlsanity.com/paper/2503.05657/) (2025)
- [Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning](https://mlsanity.com/paper/2210.01708/) (2022)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)
- [Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration](https://mlsanity.com/paper/2312.11489/) (2023)
- [Secure Federated Learning against Model Poisoning Attacks via Client Filtering](https://mlsanity.com/paper/2304.00160/) (2023)
- [Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning](https://mlsanity.com/paper/2412.03752/) (2024)
- [AugFL: Augmenting Federated Learning with Pretrained Models](https://mlsanity.com/paper/2503.02154/) (2025)
- [You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data](https://mlsanity.com/paper/2503.06916/) (2025)
- [On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations](https://mlsanity.com/paper/2411.15014/) (2024)
- [Towards cost-effective and resource-aware aggregation at Edge for Federated Learning](https://mlsanity.com/paper/2204.07767/) (2022)
- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training](https://mlsanity.com/paper/2406.15125/) (2024)
- [Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning](https://mlsanity.com/paper/2411.14937/) (2024)
- [Asynchronous Online Federated Learning with Reduced Communication Requirements](https://mlsanity.com/paper/2303.15226/) (2023)
- [Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review](https://mlsanity.com/paper/2308.13714/) (2023)
- [Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks](https://mlsanity.com/paper/2409.09273/) (2024)
- [Federated Learning Hyper-Parameter Tuning from a System Perspective](https://mlsanity.com/paper/2211.13656/) (2022)
- [FLSTRA: Federated Learning in Stratosphere](https://mlsanity.com/paper/2302.00163/) (2023)
- [Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data](https://mlsanity.com/paper/2306.10848/) (2023)
- [Optimal Batch Allocation for Wireless Federated Learning](https://mlsanity.com/paper/2404.02395/) (2024)
- [Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning](https://mlsanity.com/paper/2405.02360/) (2024)

## Abstract (arXiv)

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.