Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review

Anas Harchi; Hicham Toumi; Mohamed Talea

doi:10.35377/saucis...1826274

EN

Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review

Abstract

The rapid proliferation of Internet of Things (IoT) devices is driving significant data generation—and, consequently, a crucial demand for reliable, privacy-centric anomaly detection (AD). Federated Learning (FL) enables collaborative model training without centralizing sensitive data, addressing fundamental constraints encountered by conventional, server-oriented approaches. Adhering to the PRISMA 2020 framework, we rigorously examined 60 well-documented studies (2017–2026) on FL-oriented AD in IoT. We scrutinize architectures, learning paradigms, datasets, applications, privacy mechanisms, communication efficacy, and evaluation metrics, and systematically classify the domain into a taxonomy encompassing architectural frameworks, privacy-preserving strategies, communication protocols, and detection algorithms. In scholarly publications, FL methodologies frequently align with centralized references while preserving data confidentiality, yielding detection accuracy metrics ranging from 80% to 99.89%, with the majority of approaches exceeding 90%. Simultaneously, unresolved challenges persist: non-IID and heterogeneous data, communication and energy expenditures, resilience under adversarial circumstances, and real-world scalability. Emerging hybrid methodologies that amalgamate FL with complementary techniques (e.g., model compression, secure aggregation, and multi-modal fusion) demonstrate promise. This review delves into the contemporary landscape, exposes inadequacies, and charts explicit research directions to enhance the security and reliability of IoT environments.

Keywords

Ethical Statement

It is declared that during the preparation of this study, scientific and ethical principles were followed, and all studies used are listed in the bibliography.

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Details

Primary Language

English

Subjects

System and Network Security, Artificial Intelligence (Other)

Journal Section

Review

Authors

Anas Harchi ^*
0009-0009-7665-0425
Morocco

Hicham Toumi
0000-0003-3372-4147
Morocco

Mohamed Talea
0000-0003-2364-0804
Morocco

Early Pub Date

October 29, 2026

Publication Date

October 30, 2026

Submission Date

November 27, 2025

Acceptance Date

February 18, 2026

Published in Issue

Year 2026 Volume: 9 Number: 5

DOI

https://doi.org/10.35377/saucis...1826274

IZ

https://izlik.org/JA84NF84HK

Cite

RIS / Bibtex

APA

Harchi, A., Toumi, H., & Talea, M. (2026). Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review. Sakarya University Journal of Computer and Information Sciences, 9(5), 1686-1699. https://doi.org/10.35377/saucis...1826274

AMA

1.Harchi A, Toumi H, Talea M. Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review. SAUCIS. 2026;9(5):1686-1699. doi:10.35377/saucis.1826274

Chicago

Harchi, Anas, Hicham Toumi, and Mohamed Talea. 2026. “Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review”. Sakarya University Journal of Computer and Information Sciences 9 (5): 1686-99. https://doi.org/10.35377/saucis. 1826274.

EndNote

Harchi A, Toumi H, Talea M (October 1, 2026) Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review. Sakarya University Journal of Computer and Information Sciences 9 5 1686–1699.

IEEE

[1]A. Harchi, H. Toumi, and M. Talea, “Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review”, SAUCIS, vol. 9, no. 5, pp. 1686–1699, Oct. 2026, doi: 10.35377/saucis...1826274.

ISNAD

Harchi, Anas - Toumi, Hicham - Talea, Mohamed. “Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review”. Sakarya University Journal of Computer and Information Sciences 9/5 (October 1, 2026): 1686-1699. https://doi.org/10.35377/saucis. 1826274.

JAMA

1.Harchi A, Toumi H, Talea M. Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review. SAUCIS. 2026;9:1686–1699.

MLA

Harchi, Anas, et al. “Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 5, Oct. 2026, pp. 1686-99, doi:10.35377/saucis. 1826274.

Vancouver

1.Anas Harchi, Hicham Toumi, Mohamed Talea. Federated Learning Approaches for Anomaly Detection in IoT Environments: A Systematic Literature Review. SAUCIS. 2026 Oct. 1;9(5):1686-99. doi:10.35377/saucis. 1826274