# Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art

arXiv: https://arxiv.org/abs/2002.10610 · HTML: https://mlsanity.com/paper/2002.10610/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 19 of 19)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning](https://mlsanity.com/paper/2107.04271/) (2021)
- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems](https://mlsanity.com/paper/2112.08761/) (2021)
- [Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition](https://mlsanity.com/paper/2310.15165/) (2023)
- [On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations](https://mlsanity.com/paper/2411.15014/) (2024)
- [Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates](https://mlsanity.com/paper/2306.06559/) (2023)
- [The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study](https://mlsanity.com/paper/2307.00617/) (2023)
- [FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures](https://mlsanity.com/paper/2505.06497/) (2025)
- [Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices](https://mlsanity.com/paper/2505.08033/) (2025)
- [Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0](https://mlsanity.com/paper/2507.07613/) (2025)
- [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022)
- [Federated Adversarial Training with Transformers](https://mlsanity.com/paper/2206.02131/) (2022)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)
- [EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning](https://mlsanity.com/paper/2606.07702/) (2026)

## Abstract (arXiv)

Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing devices. As a result, vast amounts of data are generated on edge devices to drive Internet-of-Things (IoT), crowdsourcing, and other emerging technologies. The collected extensive data can be pre-processed, scaled, classified, and finally, used for predicting future events using machine learning (ML) methods. In traditional ML approaches, data is sent to and processed in a central server, which encounters communication overhead, processing delay, privacy leakage, and security issues. To overcome these challenges, each client can be trained locally based on its available data and by learning from the global model. This decentralized learning structure is referred to as Federated Learning (FL). However, in large-scale networks, there may be clients with varying computational resource capabilities. This may lead to implementation and scalability challenges for FL techniques. In this paper, we first introduce some recently implemented real-life applications of FL. We then emphasize on the core challenges of implementing the FL algorithms from the perspective of resource limitations (e.g., memory, bandwidth, and energy budget) of client clients. We finally discuss open issues associated with FL and highlight future directions in the FL area concerning resource-constrained devices.