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

TL;DR

This paper systematically reviews and evaluates federated learning techniques in edge computing, benchmarking key algorithms on datasets like MNIST and CIFAR-10 to assess their performance, robustness, and efficiency.

Contribution

It categorizes state-of-the-art FL methods, benchmarks five leading algorithms across multiple metrics, and identifies gaps for future research in edge-based FL systems.

Findings

SCAFFOLD achieves highest accuracy (0.90) and robustness.

FedAvg excels in communication and energy efficiency.

Data heterogeneity and energy limitations remain challenges.

Abstract

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.