The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Adaptive Lightweight Federated Learning With Aggregation-Only CKKS for Privacy-Preserving IoT Intrusion Detection
Abstract
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.
Related Content
|
Jiang Lv, Jiuru Lin.
© 2026.
16 pages.
|
|
Yunfei Li, Xiaodong Fu, Li Liu, Jiaman Ding, Wei Peng, Lianyin Jia.
© 2026.
40 pages.
|
|
Mahdi Ajdani.
© 2026.
19 pages.
|
|
Ibidun Christiana Obagbuwa, Madison N. Ngafeeson, Oluwatimileyin Favour Obagbuwa, Anthony Tsetse.
© 2026.
22 pages.
|
|
Xiang Gong, Qiaoqiao Wang, Guojie Li, Dehan Kong.
© 2026.
23 pages.
|
|
Jason A. Williams, Humayun Zafar, Saurabh Gupta.
© 2026.
18 pages.
|
|
Xudong Shao, Bo Yang, Zhijie Fan, Deyang Qu, Weichao Hu, Shijun Xu.
© 2026.
29 pages.
|
|
|