# A Federated Learning Framework for Healthcare IoT devices

arXiv: https://arxiv.org/abs/2005.05083 · HTML: https://mlsanity.com/paper/2005.05083/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 22 of 22)

- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (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 learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices](https://mlsanity.com/paper/2305.17005/) (2023)
- [Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach](https://mlsanity.com/paper/2510.21155/) (2025)
- [Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT](https://mlsanity.com/paper/2303.02668/) (2023)
- [Federated Learning: Issues in Medical Application](https://mlsanity.com/paper/2109.00202/) (2021)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- ["You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning](https://mlsanity.com/paper/2206.12183/) (2022)
- [FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences](https://mlsanity.com/paper/2501.13604/) (2025)
- [Communication Efficient Split Learning of ViTs with Attention-based Double Compression](https://mlsanity.com/paper/2509.15058/) (2025)
- [A Knowledge Distillation-Based Backdoor Attack in Federated Learning](https://mlsanity.com/paper/2208.06176/) (2022)
- [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions](https://mlsanity.com/paper/2408.13926/) (2024)
- [Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices](https://mlsanity.com/paper/2411.07826/) (2024)
- [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022)
- [Collaborative Machine Learning-Driven Internet of Medical Things -- A Systematic Literature Review](https://mlsanity.com/paper/2207.06416/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Contribution-Aware Bandwidth Allocation for Multimodal Split Learning](https://mlsanity.com/paper/2609.01406/) (2026)

## Abstract (arXiv)

The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devices and a centralized server. Then most of the training computation is handled by the powerful server. The sparsification of activations and gradients significantly reduces the communication overhead. Empirical study have suggested that the proposed framework guarantees a low accuracy loss, while only requiring 0.2% of the synchronization traffic in vanilla federated learning.