# Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation

arXiv: https://arxiv.org/abs/2603.08735 · HTML: https://mlsanity.com/paper/2603.08735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [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)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Privacy Risks of Securing Machine Learning Models against Adversarial Examples](https://mlsanity.com/paper/1905.10291/) (2019)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [Cross-Silo Federated Learning: Challenges and Opportunities](https://mlsanity.com/paper/2206.12949/) (2022)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://mlsanity.com/paper/2003.00199/) (2020)
- [Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses](https://mlsanity.com/paper/2402.04013/) (2024)
- [Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2012.06810/) (2020)
- [PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing](https://mlsanity.com/paper/2111.10481/) (2021)
- [Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network](https://mlsanity.com/paper/2402.18576/) (2024)
- [Spiking Neural Networks: The Future of Brain-Inspired Computing](https://mlsanity.com/paper/2510.27379/) (2025)
- [FedFog: Resource-Aware Federated Learning in Edge and Fog Networks](https://mlsanity.com/paper/2507.03952/) (2025)

## Built on by (most cited first, 1 of 1)

- [Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting](https://mlsanity.com/paper/2608.21463/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a transformative approach for distributed machine learning, particularly in edge computing environments where data privacy, low latency, and bandwidth efficiency are critical. This paper presents a systematic review and performance evaluation of FL techniques tailored for edge computing. It categorizes state-of-the-art methods into four dimensions: optimization strategies, communication efficiency, privacy-preserving mechanisms, and system architecture. Using benchmarking datasets such as MNIST, CIFAR-10, FEMNIST, and Shakespeare, it assesses five leading FL algorithms across key performance metrics including accuracy, convergence time, communication overhead, energy consumption, and robustness to non-Independent and Identically Distributed (IID) data. Results indicate that SCAFFOLD achieves the highest accuracy (0.90) and robustness, while Federated Averaging (FedAvg) excels in communication and energy efficiency. Visual insights are provided by a taxonomy diagram, dataset distribution chart, and a performance matrix. Problems including data heterogeneity, energy limitations, and repeatability still exist despite advancements. To enable the creation of more robust and scalable FL systems for edge-based intelligence, this analysis identifies existing gaps and provides an organized research agenda in the future.