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Adaptive Lightweight Federated Learning With Aggregation-Only CKKS for Privacy-Preserving IoT Intrusion Detection

Mahdi Ajdani

International Journal of Information Security and Privacy2026https://doi.org/10.4018/ijisp.402007article
ABDC C
Weight
0.50

What the paper says

Federated learning (FL) enables collaborative training without sharing raw data, but standard FL exposes client updates and burdens resource-constrained IoT devices. The authors propose AdaptiveCKKS, an FL framework combining aggregation-only CKKS encryption with index-free block sparsification and stochastic quantization. A lightweight controller adaptively selects compression ratio and quantization per device/round based on on-device calibration of bandwidth, CPU, and encryption cost, while CKKS contexts are fixed at enrollment. The server performs ciphertext-only additions, decrypting only the aggregate each round. On BoT-IoT and ToN-IoT datasets, AdaptiveCKKS improves accuracy by 3.2–3.8% over FL and fixed-HE, reduces per-round communication by ~45% and average power by ~39%, and increases resistance to membership inference and gradient inversion attacks. Results are averaged over 10 runs with 95% confidence intervals, and all artifacts are released for reproducibility.

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https://doi.org/https://doi.org/10.4018/ijisp.402007

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@article{mahdi2026,
  title        = {{Adaptive Lightweight Federated Learning With Aggregation-Only CKKS for Privacy-Preserving IoT Intrusion Detection}},
  author       = {Mahdi Ajdani},
  journal      = {International Journal of Information Security and Privacy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisp.402007},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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