# Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

arXiv: https://arxiv.org/abs/2107.06548 · HTML: https://mlsanity.com/paper/2107.06548/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Sparse Communication for Distributed Gradient Descent](https://mlsanity.com/paper/1704.05021/) (2017)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Distributed Optimization with Arbitrary Local Solvers](https://mlsanity.com/paper/1512.04039/) (2015)
- [Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems](https://mlsanity.com/paper/2007.14374/) (2020)
- [Active Learning-based Classification in Automated Connected Vehicles](https://mlsanity.com/paper/2002.07593/) (2020)
- [Semi-Stochastic Coordinate Descent](https://mlsanity.com/paper/1412.6293/) (2014)
- [Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data](https://mlsanity.com/paper/2012.05622/) (2020)

## Built on by (most cited first, 10 of 10)

- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2406.14910/) (2024)
- [Time Minimization in Hierarchical Federated Learning](https://mlsanity.com/paper/2210.04689/) (2022)
- [Network Anomaly Detection Using Federated Learning](https://mlsanity.com/paper/2303.07452/) (2023)
- [Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems](https://mlsanity.com/paper/2401.12356/) (2024)
- [Enhancing Efficiency in Multidevice Federated Learning through Data Selection](https://mlsanity.com/paper/2211.04175/) (2022)
- [Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budget](https://mlsanity.com/paper/2412.03385/) (2024)
- [Personalized Multi-tier Federated Learning](https://mlsanity.com/paper/2407.14251/) (2024)
- [NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces](https://mlsanity.com/paper/2609.09786/) (2026)

## Abstract (arXiv)

Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in IoT heterogeneous systems and propose an optimized solution for user assignment and resource allocation on multiple edge nodes. In particular, this work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. In particular, our numerical results highlight the effectiveness of our approach and its ability to provide 4-6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75-85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.