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Papers on “federated learning privacy preserving”

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1. Improving LoRA in Privacy-preserving Federated Learning

   Youbang Sun, Zitao Li, Yaliang Li, et al. · 2024 · ArXiv · 227 cites

   Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model module. However, when applied in the setting of privacy-preserving federated learning (FL), LoRA may become unstable due to the following facts: 1) the effects of data heterogeneity and multi-step local updates are non-negligible, 2) additive noise enforced on updating gradients to guarantee differential privacy (DP) can be amplified and 3) the final performance is susceptible to hy

2. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings

   Rahul Haripriya, Nilay Khare, Manish Pandey · 2025 · Scientific Reports · 106 cites

   Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. In fact, over 30% of healthcare organizations globally have experienced a data breach in the last year, highlighting the need for secure solutions. This study investigates the integration of transfer learning and federated learning for privacy-preserving medical image classification using GoogLeNet and VGG16 as baseline models to evaluate the generalizability of the proposed framework. Pre-trained on ImageNet and fine-tuned on three specialized medical datasets for TB chest X-rays, brain tumor MRI scans, and diabetic retinopathy images

3. A Review on Federated Learning Architectures for Privacy-Preserving AI: Lightweight and Secure Cloud–Edge–End Collaboration

   Shanhao Zhan, Lianfen Huang, Gaoyu Luo, et al. · 2025 · Electronics · 79 cites

   Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into

4. Generative Federated Learning With Small and Large Models in Consumer Electronics for Privacy-Preserving Data Fusion in Healthcare Internet of Things

   Taher M. Ghazal, Shayla Islam, M. Hasan, et al. · 2025 · IEEE Transactions on Consumer Electronics · 62 cites

   Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated Data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. Th

5. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.

   Nikolas Koutsoubis, A. Waqas, Yasin Yilmaz, et al. · 2025 · Radiology. Artificial intelligence · 54 cites

   "Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Artificial Intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinic

6. Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities

   Mahmoud Ragab, E. B. Ashary, Bandar M. Alghamdi, et al. · 2025 · Scientific Reports · 52 cites

   With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these c

7. Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy Preserving and Real-Time Threat Detection Capabilities

   Milad Rahmati, Antonino Pagano · 2025 · Informatics · 46 cites

   The rapid expansion of the Internet of Things (IoT) ecosystem has transformed industries but also exposed significant cybersecurity vulnerabilities. Traditional centralized methods for securing IoT networks struggle to balance privacy preservation with real-time threat detection. This study presents a Federated Learning-Driven Cybersecurity Framework designed for IoT environments, enabling decentralized data processing through local model training on edge devices to ensure data privacy. Secure aggregation using homomorphic encryption supports collaborative learning without exposing sensitive information. The framework employs GRU-based recurrent neural networks (RNNs) for anomaly detection,

8. Federated Learning With Small and Large Models With Privacy-Preserving Data Space for Holographic Internet of Things in Consumer Electronics

   Taher M. Ghazal, M. Hasan, Abdelrahman H. Hussein, et al. · 2025 · IEEE Transactions on Consumer Electronics · 45 cites

   Holographic Internet of Things (IoT) aggregates virtual and augmented reality to provide real-time modeling that improves the user experience of consumer electronic products and applications. The incorporated technologies support third-party applications for which heterogeneous privacy-preserving features are required. Considering this factor, a Modeling Space Privacy (MSP) is introduced in this article using ternary homomorphic encryption (THE). The proposed privacy scheme encourages space and component privacy using independent hashes using HE. Privacy is retained using the ternary operation between the components and space to ensure maximum security of IoT model representations. Third-par