# Federated Edge Learning : Design Issues and Challenges

arXiv: https://arxiv.org/abs/2009.00081 · HTML: https://mlsanity.com/paper/2009.00081/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [FedICT: Federated Multi-task Distillation for Multi-access Edge Computing](https://mlsanity.com/paper/2301.00389/) (2023)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration](https://mlsanity.com/paper/2312.11489/) (2023)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Blind Federated Learning via Over-the-Air q-QAM](https://mlsanity.com/paper/2311.04253/) (2023)
- [Optimising Communication Overhead in Federated Learning Using NSGA-II](https://mlsanity.com/paper/2204.02183/) (2022)
- [Federated Learning for Water Consumption Forecasting in Smart Cities](https://mlsanity.com/paper/2301.13036/) (2023)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Data-Aware Device Scheduling for Federated Edge Learning](https://mlsanity.com/paper/2102.09491/) (2021)
- [The Impact of Cut Layer Selection in Split Federated Learning](https://mlsanity.com/paper/2412.15536/) (2024)
- [Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning](https://mlsanity.com/paper/2104.03169/) (2021)
- [Knowledge Distillation in Federated Edge Learning: A Survey](https://mlsanity.com/paper/2301.05849/) (2023)
- [Towards a Secure and Reliable Federated Learning using Blockchain](https://mlsanity.com/paper/2201.11311/) (2022)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [Blind Asynchronous Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2210.17469/) (2022)
- [Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architectures](https://mlsanity.com/paper/2509.03695/) (2025)
- [Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients](https://mlsanity.com/paper/2310.07171/) (2023)
- [FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation](https://mlsanity.com/paper/2405.13378/) (2024)
- [Peak-Controlled Logits Poisoning Attack in Federated Distillation](https://mlsanity.com/paper/2407.18039/) (2024)
- [Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation](https://mlsanity.com/paper/2410.08651/) (2024)
- [Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration](https://mlsanity.com/paper/2501.00693/) (2025)
- [Fairness in Federated Learning: Fairness for Whom?](https://mlsanity.com/paper/2505.21584/) (2025)
- [Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations](https://mlsanity.com/paper/2207.07700/) (2022)
- [QFed: Parameter-Compact Quantum-Classical Federated Learning](https://mlsanity.com/paper/2601.09809/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalability. However, implementing FL at the network edge is challenging due to system and data heterogeneity and resources constraints. In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL). The design of FEEL algorithms for resources-efficient learning raises several challenges. These challenges are essentially related to the multidisciplinary nature of the problem. As the data is the key component of the learning, this article advocates a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a general framework for the data-aware scheduling as a guideline for future research directions. We also discuss the main axes and requirements for data evaluation and some exploitable techniques and metrics.