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 frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.
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 clients' private data from local models and explore sensitive knowledge from global model. Trusted Execution Environment (TEE) based schemes offer secure enclaves to protect models privacy during the aggregation procedure. However, TEE technologies incur new functional and security assumptions, where a single vulnerability could undermine the trusted system. Cryptographic schemes such as homomorphic encryption (HE), differential privacy (DP) and secret sharing (SS) cannot achieve the simultaneous requirements of efficiency, accuracy and privacy currently. In this paper, we present DoshFL that avoids expensive cryptographic operations and enables tunable trade-off between privacy and efficiency in federated learning for model asset protection.
Jia-Ming Fang· 2026 3rd World Conference on...· 0 citations
Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. Our FL framework is designed to address these challenges while proposing a modular, flexible and micro-service architecture. More precisely, it 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. This allows blind model aggregation by an orchestration server, requiring a minimum of $t$-out-of-$N$ active clients for decryption while minimizing communication overhead thanks to both cryptographic and network protocols. We ensure IND-CPA-D security through noise flooding and mitigate the recent key-recovery attack on synchronized decryptors by renewing the collective key material at every round. We demonstrate the framework's effectiveness through diverse use cases, ranging from standard image recognition (EMNIST) to complex genomic classification including breast cancer subtyping on TCGA, evaluating system performance across different threshold values and model scales.
P. Largillier, Karl Paygambar, Cédric Gouy-Pailler et al.· Proceedings of the 23rd Inte...· 0 citations
This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning (FL) systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information may still be leaked during model updates. To address this issue, homomorphic encryption enables computations on encrypted data, while differential privacy prevents the extraction of individual information through statistical techniques applied to model outputs. The proposed architecture was tested on the Framingham, Pima Indians Diabetes, and Bank Marketing datasets, revealing that enhanced privacy can be achieved without significantly compromising model accuracy. Furthermore, the impact of data heterogeneity among clients on model performance was analyzed, and 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. The findings demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensitive domains such as healthcare and finance.
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
With the rapid evolution of 5G communications, artificial intelligence, and new energy technologies, intelligent driving has become a pivotal component of modern transportation infrastructure. However, the openness and inherent complexity of the Internet of Vehicles (IoV) pose significant challenges to data privacy and security. While Federated Learning (FL) facilitates collaborative training of data models without requiring raw data to leave local devices, its centralized aggregation architecture struggles to address privacy concerns in cross-trust domain collaborations and fails to effectively handle the challenges posed by vehicle node mobility. In this paper, we investigate cross-trust domain collaborative federated learning within the IoV and construct a privacy-preserving Cloud-Edge-End architecture based on Fully Homomorphic Encryption (FHE). To address the dynamic nature of vehicle nodes, we propose a hybrid privacy-preserving scheme that integrates Threshold Fully Homomorphic Encryption (ThFHE) and Multi-Key Fully Homomorphic Encryption (MKFHE). The experimental results indicate that the proposed collaborative architecture is theoretically reasonable. In addition, the scheme effectively achieves collaborative model training while maintaining security at the privacy protection level and supporting seamless vehicle mobility.
Bo-Ya Liu, Guosheng Xu, Chenyu Wang et al.· Electronics· 0 citations