Federated learning-based lightweight digital certificate authentication and anomaly detection framework for IoT environments

The exponential growth of Internet of Things (IoT) deployments has strained conventional security architectures, exposing two interlinked weaknesses: certificate handling designed for servers overwhelms microcontroller-class hardware, and isolated anomaly detection leaks privacy once behavioural data leaves the device. This paper addresses both within one architecture, combining elliptic curve Qu–Vanstone (ECQV) implicit certificates with privacy-preserving collaborative anomaly detection across device, edge and cloud tiers. The certificate occupies 88 bytes, a 92.7% reduction relative to a typical X.509 credential, and a complete authentication exchange, comprising public-key reconstruction, a verifier challenge, proof of private-key possession over the bound transcript and session-key confirmation, completes in 12.3 ms on an ESP32, roughly five times faster than an RSA-2048 baseline measured on the same board. On the detection side, similarity-based aggregation weighting accommodates the non-IID character of distributed traffic, while per-example gradient clipping with Gaussian noise provides differential privacy whose budget we account for with a Renyi accountant and report in full. Evaluation on the N-BaIoT dataset, roughly 7 million records from nine commercial devices under Mirai and BASHLITE traffic, yields 95.2% detection accuracy, 1.6 percentage points below the centralized upper bound. Throughput stays within 15% of its peak as the network grows from 100 to 1000 devices, the larger configurations being reached by adding edge aggregators rather than by loading a single one. Ablation studies show that adaptive weighting recovers 4.7 percentage points under strong label skew and that coupling authentication with detection halves the false negative rate against stolen credentials; comparison with Krum, trimmed-mean and FLTrust shows, conversely, that the proposed soft weighting is not a substitute for a Byzantine-robust rule once more than 15% of clients misbehave. The trust root remains the cloud certificate authority; what the design decentralizes is the verification path, not the root itself.

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Publication Details

Journal
Scientific Reports
Published
2026-08-14
DOI
https://doi.org/10.1038/s41598-026-67014-x
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

Federated learning-based lightweight digital certificate authentication and anomaly detection framework for IoT environments

Wenlong Han, Pengfei Tao, Muheng Sui, Donghong Zheng et al.
Scientific Reports
Privacy-Preserving Technologies in Data
article

Federated learning-based lightweight digital certificate authentication and anomaly detection framework for IoT environments

Wenlong Han, Pengfei Tao, Muheng Sui, Donghong Zheng, Wenjun Li, Yi Gao
article en

Abstract

The exponential growth of Internet of Things (IoT) deployments has strained conventional security architectures, exposing two interlinked weaknesses: certificate handling designed for servers overwhelms microcontroller-class hardware, and isolated anomaly detection leaks privacy once behavioural data leaves the device. This paper addresses both within one architecture, combining elliptic curve Qu–Vanstone (ECQV) implicit certificates with privacy-preserving collaborative anomaly detection across device, edge and cloud tiers. The certificate occupies 88 bytes, a 92.7% reduction relative to a typical X.509 credential, and a complete authentication exchange, comprising public-key reconstruction, a verifier challenge, proof of private-key possession over the bound transcript and session-key confirmation, completes in 12.3 ms on an ESP32, roughly five times faster than an RSA-2048 baseline measured on the same board. On the detection side, similarity-based aggregation weighting accommodates the non-IID character of distributed traffic, while per-example gradient clipping with Gaussian noise provides differential privacy whose budget we account for with a Renyi accountant and report in full. Evaluation on the N-BaIoT dataset, roughly 7 million records from nine commercial devices under Mirai and BASHLITE traffic, yields 95.2% detection accuracy, 1.6 percentage points below the centralized upper bound. Throughput stays within 15% of its peak as the network grows from 100 to 1000 devices, the larger configurations being reached by adding edge aggregators rather than by loading a single one. Ablation studies show that adaptive weighting recovers 4.7 percentage points under strong label skew and that coupling authentication with detection halves the false negative rate against stolen credentials; comparison with Krum, trimmed-mean and FLTrust shows, conversely, that the proposed soft weighting is not a substitute for a Byzantine-robust rule once more than 15% of clients misbehave. The trust root remains the cloud certificate authority; what the design decentralizes is the verification path, not the root itself.

Scientific Reports
Suzhou Vocational Institute of Industrial Technology (CN), 421 Hospital of PLA (CN)
Government of Jiangsu Province, Qinglan Project of Jiangsu Province of China
Openalex Percentile: Top 7%
Privacy-Preserving Technologies in Data
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