Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection
Billions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. At its core is...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11095679/ |
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| author | Chandu Gutti Karthik Thumula Parag Balbudhe |
| author_facet | Chandu Gutti Karthik Thumula Parag Balbudhe |
| author_sort | Chandu Gutti |
| collection | DOAJ |
| description | Billions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. At its core is Hybrid Adaptive-Weight Aggregation (HADA), a new server-side rule that weights client updates by their SHAP-based feature stability and individual differential-privacy (DP) budgets. HADA down-weights noisy or low-privacy updates, speeding convergence and hardening the global model against poisoning. Evaluated on the CIC-BCCC-NRC TabularIoTAttack-2024 and Edge-IIoTset benchmarks with 100 simulated ARM-class devices, the proposed FL-IDS achieves 85–89 % detection accuracy—on par with centralized training—while reducing per-round uplink traffic by an order of magnitude. Under strong adversarial perturbations (FGSM, PGD-10, label-flip) it retains 66–73 % accuracy, outperforming vanilla FedAvg by up to 22 percentage points. A formal convergence theorem is proved for non-IID, DP-noisy updates, and the privacy–utility curve shows <inline-formula> <tex-math notation="LaTeX">$\lt \!1.5$ </tex-math></inline-formula> pp accuracy loss at <inline-formula> <tex-math notation="LaTeX">$\varepsilon = 1.0$ </tex-math></inline-formula>. These results demonstrate that HADA-FL delivers a scalable, privacy-preserving, and attack-resilient intrusion-detection system suitable for real-world heterogeneous IoT deployments. |
| format | Article |
| id | doaj-art-2da0ea0d3ab14c18baf328fe593b6442 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-2da0ea0d3ab14c18baf328fe593b64422025-08-20T04:01:15ZengIEEEIEEE Access2169-35362025-01-011313586313587510.1109/ACCESS.2025.359248111095679Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion DetectionChandu Gutti0https://orcid.org/0009-0001-5968-5898Karthik Thumula1https://orcid.org/0009-0001-2690-8901Parag Balbudhe2https://orcid.org/0009-0005-7594-5767Independent Researcher, San Jose, CA, USAIndependent Researcher, San Jose, CA, USAIndependent Researcher, Boston, MA, USABillions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. At its core is Hybrid Adaptive-Weight Aggregation (HADA), a new server-side rule that weights client updates by their SHAP-based feature stability and individual differential-privacy (DP) budgets. HADA down-weights noisy or low-privacy updates, speeding convergence and hardening the global model against poisoning. Evaluated on the CIC-BCCC-NRC TabularIoTAttack-2024 and Edge-IIoTset benchmarks with 100 simulated ARM-class devices, the proposed FL-IDS achieves 85–89 % detection accuracy—on par with centralized training—while reducing per-round uplink traffic by an order of magnitude. Under strong adversarial perturbations (FGSM, PGD-10, label-flip) it retains 66–73 % accuracy, outperforming vanilla FedAvg by up to 22 percentage points. A formal convergence theorem is proved for non-IID, DP-noisy updates, and the privacy–utility curve shows <inline-formula> <tex-math notation="LaTeX">$\lt \!1.5$ </tex-math></inline-formula> pp accuracy loss at <inline-formula> <tex-math notation="LaTeX">$\varepsilon = 1.0$ </tex-math></inline-formula>. These results demonstrate that HADA-FL delivers a scalable, privacy-preserving, and attack-resilient intrusion-detection system suitable for real-world heterogeneous IoT deployments.https://ieeexplore.ieee.org/document/11095679/Federated learningIoT securityintrusion detection systemprivacy-preservingdistributed learningcybersecurity |
| spellingShingle | Chandu Gutti Karthik Thumula Parag Balbudhe Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection IEEE Access Federated learning IoT security intrusion detection system privacy-preserving distributed learning cybersecurity |
| title | Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection |
| title_full | Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection |
| title_fullStr | Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection |
| title_full_unstemmed | Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection |
| title_short | Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection |
| title_sort | federated learning for distributed iot security a privacy preserving approach to intrusion detection |
| topic | Federated learning IoT security intrusion detection system privacy-preserving distributed learning cybersecurity |
| url | https://ieeexplore.ieee.org/document/11095679/ |
| work_keys_str_mv | AT chandugutti federatedlearningfordistributediotsecurityaprivacypreservingapproachtointrusiondetection AT karthikthumula federatedlearningfordistributediotsecurityaprivacypreservingapproachtointrusiondetection AT paragbalbudhe federatedlearningfordistributediotsecurityaprivacypreservingapproachtointrusiondetection |