Jun 2026· 2026 11th International Conference on Electronic Technology and Information Science (ICETIS)· pp. 1-8· 0 citations· 31 references
Abstract
The Industrial Internet of Things (IIoT) generates massive data for intelligent decisions like predictive maintenance in smart manufacturing. However, this data often contains sensitive information, requiring robust privacy protection under regulations such as GDPR and ISO/IEC 29100. Centralized training transmits raw data, incurring heavy communication overhead-conflicting with limited IIoT bandwidth-and increasing breach risks. Federated Learning (FL), where “models move, data remains local,” suits IIoT collaborative training. Yet existing privacypreserving FL (PPFL) schemes have shortcomings. Plain homomorphic encryption (PHE) lacks flexibility and incurs high overhead. Multi-key homomorphic encryption (MKHE) partially addresses these issues but fails to resolve ciphertext expansion and communication costs. Neither approach handles dynamic device membership, common in IIoT. Solutions integrating both privacy and verifiable aggregation remain scarce in dynamic settings. To address these challenges, this paper proposes a tailored FL framework with three key innovations: 1)Optimized MKHE: Reduces overhead via parameter pruning. 2)Dynamic membership management: Enables seamless device join and exit. 3)HS-FC integration: Achieves constant-time verifiable aggregation.
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequent...
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
Federated learning (FL) is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. However, this centralization of sensitive model updates incurs security and privacy issues. A malicious aggregator can launch inference attacks to violate the privacy of clien...
Jia-Ming Fang· 2026 3rd World Conference on...· 0 citations
The FL framework integrates an efficient gRPC communication layer and a Finite State Machine to ensure robust component synchronization and threat detection, while relying on a fault-tolerant secure aggregation protocol using a Threshold variant of the CKKS homomorphic cryptosystem.
P. Largillier, Karl Paygambar, Cédric Gouy-Pailler et al.· International Conference on...· 0 citations
It was concluded that strategies such as the careful selection of differential privacy parameters and training settings, along with the use of larger datasets, can improve the efficiency of FL and demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensiti...
Cagdas Karatas, Hibanur Karadogan, A. Ertug et al.· 0 citations
This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.
Harsh Kasyap, Anil Kumar Pradhan, U. Atmaca et al.· 0 citations
A hybrid privacy-preserving scheme that integrates Threshold Fully Homomorphic Encryption (ThFHE) and Multi-Key Fully Homomorphic Encryption (MKFHE) and effectively achieves collaborative model training while maintaining security at the privacy protection level and supporting seamless vehicle mobility is proposed.
Bo-Ya Liu, Guosheng Xu, Chenyu Wang et al.· Electronics· 0 citations
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