mlsanityData to 7 Oct 2026

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

arXiv 2603.08735 ↗First posted 24 Feb 2026cs.DC cs.AIcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 24 Feb 2026.

Builds on

31 of 53 references.

References · 31

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19 more
  1. 2019Asynchronous Federated Optimizationcited by 264
  2. 2019Can You Really Backdoor Federated Learning?cited by 250
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
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  19. 2025A Hybrid Deep Learning and Forensic Approach for Robust Deepfake Detectioncited by 1

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