Evaluation of Recent Developments in Federated Learning

Yuhao Lu

doi:10.54254/2755-2721/2025.LD25894

Applied and Computational EngineeringOpen access

Evaluation of Recent Developments in Federated Learning

Research Article

Open Access

Evaluation of Recent Developments in Federated Learning

Yuhao Lu ^1*

¹ College of Science, Mathematics and Technology, Wenzhou - Kean University, No.88 Daxue Road, Ouhai District, Wenzhou City, Zhejiang Province, China, 325060

^*Corresponding author: luyuh@kean.edu

Published on 13 August 2025

ACE Vol.184

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

Federated learning (FL) is a crucial technology for healthcare, IoT, and finance applications. This paper evaluates recent advancements in FL from 2023 to 2025, focusing on optimization algorithms, privacy-preserving techniques, communication efficiency, and real-world applications. It compares algorithms like FedAvg, FedProx, SCAFFOLD, and FedDyn, assessing their performance under data heterogeneity and communication constraints. Privacy techniques like differential privacy and secure aggregation are evaluated for accuracy and computational overhead. Communication-efficient methods and real-world deployments are also analyzed. The evaluation offers actionable insights for selecting appropriate FL methods for specific use cases.

Keywords:

Federated learning, optimization algorithms, privacy preservation, communication efficiency, critical evaluation

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References

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Cite this article

Lu,Y. (2025). Evaluation of Recent Developments in Federated Learning. Applied and Computational Engineering,184,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.184

ISSN: 2755-2721(Print) / 2755-273X(Online)

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