2026· EPJ Web of Conferences· 0 citations· 5 references
TL;DR
SplittingFed-DP relocates the Gaussian DP mechanism from the high-dimensional gradient to the low-dimensional activation space at the cut layer, audited under Rényi differential privacy and proves that this same Gaussian release coincides with the randomised-smoothing operator of Cohen et al. at the cut layer.
Abstract
Federated learning for IoT intrusion detection must protect each client's training records, yet the standard remedy (DP-SGD on full-encoder gradients) collapses utility under non-IID, label-scarce traffic because the Gaussian noise is injected into a parameter space of 10
5
dimensions. We propose
SplitFed-DP,
a federated split-learning pipeline with a
dual
guarantee. First, it relocates the Gaussian DP mechanism from the high-dimensional gradient to the low-dimensional activation space at the cut layer, audited under Rényi differential privacy; this recovers tens of points of accuracy and macro-F1 over gradient-level DP-SGD and, at a fixed privacy budget, statistically ties non-private federated baselines that pay no privacy cost. Second, we prove that this same Gaussian release coincides with the randomised-smoothing operator of Cohen et al. at the cut layer, so a single training run yields both (ɛ,δ)-DP and a certified l
2
-robustness guarantee in input space at no additional training cost. On TON-IoT and Bot-IoT, S
plit
F
ed
-DP closes about two-thirds of the DP-SGD-to-oracle utility gap and certifies a large majority of inputs at non-trivial radii. All reported numbers come from executed runs; no values are interpolated.
The rapid development of the Industrial Internet of Things (IIoT) has transformed the current industrial control systems (ICS), but in the process has revealed operational technology (OT) to advanced cyber-threats. Conventional centralized intrusion detection systems (IDS) demand the pooling of massive telemetry traffic, a requirement that contributes to prohibitive communication latencies and breaches strict corporate data privacy requirements. Although Federated Learning (FL) provides a more decentralized alternative by training the models locally on edge gateways, it performs badly in non-Identically and Independently Distributed (non-IID) network traffic a condition ubiquitous in heterogeneous factory setting. The paper suggests a privacy-preserving, robust, localized deep learning-based IDS, which utilizes Federated Proximal (FedProx) optimization. The framework is based on multi-layer feed-forward neural network architecture, only that it incorporates parameterized proximal regularization term (mu = 0.5) as a part of local loss function to penalize client parameters deviation and eliminate client drift phenomenon.
Unknown authors· International Journal of Res...· 0 citations
Healthcare Internet of Things (HIoT) deployments generate sensitive patient telemetry data on resource-constrained edge devices, which are prime targets for network intrusions. Centralizing raw telemetry for training intrusion detection system (IDS) models violates patient privacy and contravenes data-protection regulations such as HIPAA and GDPR. This paper proposes PPFL-IDS, a Privacy-Preserving Federated Learning framework for intrusion detection in HIoT environments. PPFL-IDS combines federated model aggregation with differential privacy noise injection and secure aggregation protocols to train a lightweight gradient-boosted ensemble IDS without exposing local device data. A heterogeneity-aware client selection mechanism addresses the challenge of non-independent and identically distributed (non-IID) data inherent in multi-site HIoT deployments. Evaluated on the UNSW-NB15 and a synthetic HIoT dataset spanning five attack categories, PPFL-IDS achieves a weighted F1-score of 0.938 and a mean detection latency of 20.3 ms, outperforming FedAvg, FedProx, and SCAFFOLD baselines while satisfying an ε-differential privacy budget of 1.2. Results demonstrate that strong privacy guarantees and high detection accuracy can be achieved simultaneously in federated HIoT security architectures.
Nutan Gusain, J. Alzubi· International Journal on Com...· 0 citations
A hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning is proposed and can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.
Huan Yin, Cong Chen, Jing-Yi Zhang et al.· Italian National Conference...· 0 citations
Federated Learning has become a practical approach for training intrusion detection models across distributed Internet of Things devices, but it remains exposed to poisoning attacks, non-IID data heterogeneity, and free-rider exploitation. This paper presents DT-Guard, a defense framework that leverages a server-side Digital Twin as a controlled testing environment for actively verifying client model behavior. Each submitted update is deployed in the Digital Twin and evaluated on synthetic challenge data through a four-layer pipeline that examines detection capability, backdoor resistance, parameter deviation, and cross-round stability. A complementary aggregation scheme called DT-Driven Performance Weighting compares client predictions against the current global model, exposing free-riders whose outputs are nearly indistinguishable from the global baseline. We validate DT-Guard on CIC-IoT-2023 under five poisoning strategies. DT-Guard generally outperforms nine existing defenses in accuracy, false positive rate, and contribution fairness.
H. Pham, Duy The Phan, Van-Hau Pham· IEEE International Conferenc...· 0 citations
Training machine learning models across many institutions without moving raw data is attractive in theory, but the standard Federated Learning setup hands enormous power to one central aggregation server that participants must trust blindly. what happens when that trust assumption is violated and build a system, that does not require it. couples a four-layer distributed architecture with three stacked privacy mechanisms Local Differential Privacy to bound what any single gradient reveals, Secure Multi-Party Computation via the SecAgg protocol so the aggregation server never sees individual updates, and Tendermint-BFT blockchain consensus to create a tamper-evident audit log that no single party controls. Our main technical contribution is a formal proof that the combination of LDP and SecAgg satisfies end-to-end $(\varepsilon, \delta)$-differential privacy, including the SecAgg masking step cannot degrade the DP guarantee already earned by LDP noise injection. On CIFAR-10 with a ResNet-18 under three Dirichlet non-IID splits tested on real cluster hardware, reached 78.3% accuracy at $\varepsilon=0.83$ against six baselines including FedProx and SCAFFOLD-two standard non-IID benchmarks that prior blockchain FL work has consistently omitted.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
The proliferation of Internet of Things (IoT) devices has amplified the attack surface for large-scale cyber threats, with rapidly evolving malware families such as Mirai posing significant detection challenges. Existing side-channel and host-based approaches are limited by poor generalization to unseen variants and the absence of robust privacy protections during training. In this paper, we propose SecShield, a federated deep learning framework for privacy-preserving malware detection in resource-constrained IoT environments. SecShield employs a three-party secure computation model, where clients locally train a shared deep neural network on power side-channel traces and transmit only noisy parameter updates to a central server via an aggregator, preventing raw data exposure. Differential privacy is incorporated by injecting calibrated noise into gradients, mitigating inference risks such as data reconstruction and poisoning attacks. This decentralized architecture enables continuous adaptation to zero-day malware, reduces communication overhead, and ensures robustness against adversarial manipulation. Experimental evaluations on IoT power trace datasets demonstrate that SecShield achieves high detection accuracy over 98% while preserving client data confidentiality and maintaining computational efficiency.
Damodar Dhital, Sabir Ahmed Khan, Almustapha A. Wakili et al.· International Conference on...· 0 citations
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