Computer Science · Literature

Research papers on Federated learning and privacy

Recent and highly-cited academic work on federated learning and privacy, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. Improving LoRA in Privacy-preserving Federated Learning

   You-Bang Sun, Zitao Li, Ya-Liang Li, et al. · 2024 · ArXiv · 253 citations

   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

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

   Rahul Haripriya, Nilay Khare, M. Pandey · 2025 · Scientific Reports · 131 citations

   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

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

   Shanhao Zhan, Lian-Fen Huang, Gaoyu Luo, et al. · 2025 · Electronics · 89 citations

   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

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

   Nikolas Koutsoubis, A. Waqas, Yasin Yilmaz, et al. · 2025 · Radiology. Artificial intelligence · 67 citations

   "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

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

   M. Ragab, E. B. Ashary, Bandar M. Alghamdi, et al. · 2025 · Scientific Reports · 60 citations

   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

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6. Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy

   Ansam Khraisat, Ammar Alazab, M. Alazab, et al. · 2025 · Discover Internet of Things · 58 citations

   The rapid expansion of IoT devices has introduced significant cybersecurity risks, as attackers increasingly exploit these networks’ vulnerabilities. To counter this threat, this paper presents the Privacy-Enhanced IoT Defence System (PEIoT-DS), a novel solution that emphasises data privacy while delivering high-performance intrusion detection. PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. IoT devices only contribute model updates, which are then combined to improve the global model. While allowing devices to benefit from the network’s collective insights, this decentralised learning

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

   Milad Rahmati, Antonino Pagano · 2025 · Informatics · 53 citations

   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,

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8. A Verifiable Privacy-Preserving Federated Learning Framework Against Collusion Attacks

   Yange Chen, Suyu He, Bao-Cang Wang, et al. · 2025 · IEEE Transactions on Mobile Computing · 46 citations

   Most of the current federated learning schemes aimed at safeguarding privacy exhibit vulnerability to collusion attacks and lack a verification mechanism for participants to consolidate the aggregation results of the parameter server, leading to privacy breaches for users and inaccurate model training outcomes. In order to address these issues, we propose a verifiable privacy-preserving federated learning framework against collusion attacks. Primarily, the federated learning scheme is reconfigured utilizing the ElGamal encryption algorithm, which effectively safeguards the data privacy of participants in scenarios involving collusion between certain participants and servers. Additionally, th

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

   Taher M. Ghazal, M. K. Hasan, Abdelrahman H. Hussein, et al. · 2025 · IEEE Transactions on Consumer Electronics · 46 citations

   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

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10. Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction

    Hui-Ya Zhao, De-Hao Sui, Ya-Sha Wang, et al. · 2025 · Sensors (Basel, Switzerland) · 38 citations

    Secure and privacy-preserving health status representation learning has become a critical challenge in clinical prediction systems. While deep learning models require substantial high-quality data for training, electronic health records are often restricted by strict privacy regulations and institutional policies, particularly during emerging health crises. Traditional approaches to data integration across medical institutions face significant privacy and security challenges, as healthcare providers cannot directly share patient data. This work presents MultiProg, a secure federated learning framework for clinical representation learning. Our approach enables multiple medical institutions to

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11. Federated Learning for Cybersecurity: A Privacy-Preserving Approach

    E. Timofte, M. Dimian, A. Graur, et al. · 2025 · Applied Sciences · 31 citations

    The growing number of cyber threats and the implementation of stringent privacy regulations have revealed significant shortcomings in traditional centralized machine learning models, especially in distributed systems like the Internet of Things (IoT). This study presents a Federated Learning (FL) framework designed for intrusion detection and malware classification. This framework enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight, privacy-preserving techniques, including gradient clipping, differential privacy, and encrypted model aggregation, to ensure secure and efficient collaborati

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12. AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

    Jie Huang, Cheng Xu, Zhao-Hua Ji, et al. · 2022 · Security and Communication Networks · 28 citations

    Federated learning can effectively protect local data privacy in 5G-V2X environment and ensure data protection in Internet of vehicles environment. The advantages of low delay of 5G network should be better utilized in the vehicle-road cooperative system. But the existing asynchronous federated learning obtains a local model through different node training and completes the update of the global model through the central server. There are problems such as single point of failure, privacy leakage, and deviation of aggregation parameters. In response to the above problems, we proposed a 5G-V2X-oriented asynchronous federated learning privacy-preserving computing model (AFLPC). We used an adapti

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13. When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System

    Bowen Zhao, Xi-Meng Liu, Wei-neng Chen · 2021 · ArXiv · 26 citations

    Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than that of data processing. To tackle faced problems of MCS, in this paper, we integrate federated learning (FL) into MCS and propose a privacy-preserving MCS system, called \textsc{CrowdFL}. Specifically, in order to protect privacy, participants locally process sensing data via federated learning and only upload encrypted training models. Particula

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14. Decentralized Federated Learning Privacy-Preserving Framework Based on Blockchain

    De-Ling Huang, Yuqiao Yang, Yifan Huang · 2023 · 2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC) · 6 citations

    Federated Learning is a promising approach for privacy-preserving distribute training, but it still faces trust issues and privacy concerns. To address such problems, we suggest a decentralized federated learning framework (PP-BFL) based on blockchain that introduces a node reputation value mechanism to establish a trusted reputation system and a training accuracy verification mechanism that leverages smart contracts to ensure model accuracy while protecting data privacy. Our framework reduces the risk of privacy violations due to collusive evil participants and encourages honest and active participation by modeling historical performance and ranking participants based on their trustworthine

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15. Federated Learning for Privacy-Preserving AI: A Comparative Analysis of Decentralized Data Training

    Dr. Subash Ranjan Kabat · 2025 · International Journal of Machine Learning, AI & Data Science Evolution · 4 citations

    The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw data. This paper explores the principles, advantages, and challenges of FL and conducts an empirica

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16. Federated Learning for Privacy-Preserving AI

    RN Shashi Vardhan · 2025 · International Journal of Research Publication and Reviews · 2 citations

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17. Federated Learning Architectures for Privacy Preserving Financial Fraud Detection Systems

    Favour . C. Ezeugboaja · 2025 · Frontiers in Emerging Artificial Intelligence and Machine Learning · 2 citations

    The increasing complexity and intensity of cases of financial fraud, such as synthetic identity fraud and international money laundering, have become significant concerns for classic fraud detection solutions, especially under strict data privacy regulations such as GDPR or EU Artificial Intelligence Act guidelines. This study focuses on the very pressing need to pursue high fraud detection performance while simultaneously ensuring user data confidentiality for highly fragmented financial systems. The study uses federated learning (FL) designs to analyse interesting opportunities for possibly entirely decentralized machine learning functions among diverse financial agencies without any need

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18. Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments

    Bangar Raju Cherukuri · 2024 · International Journal of Science and Research (IJSR) · 1 citations

    Federated Learning (FL) is a relatively new type of decentralized ML developed to tackle privacy problems inherent to centralized ML methodologies. Thus, FL allows a model to train across multiple distributed devices or edge nodes without exchanging raw data. This approach maintains user privacy since data must not be transmitted to a central location. Only new model parameters are received and recombined to make a global model. This paper looks at the workings of FL to show how it can improve the security of often sensitive data, especially with artificial intelligence solutions based on cloud platforms. The role of federated learning is most prominent in the sectors that work with informat

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19. Federated Learning; Privacy Preserving Machine Learning for Decentralized Data

    Jitendra Singh Chouhan, Amit Kumar Bhatt, Nitin An · 2023 · Tuijin Jishu/Journal of Propulsion Technology · 1 citations

    Federated learning represents a compelling solution for tackling the privacy challenges inherent in decentralized and distributed environments when it comes to machine learning. This scholarly paper delves deep into the realm of federated learning, encompassing its applications and the latest privacy-preserving techniques used for training machine learning models in a decentralized manner. We explore the reasons behind the adoption of federated learning, highlight its advantages over conventional centralized approaches, and examine the diverse methods employed to safeguard privacy within this framework. Furthermore, we scrutinize the current obstacles, unresolved research queries, and

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20. Privacy Preserving Federated Learning Efficiency Optimization Algorithm based on Differential Privacy

    Rui Xie · 2025 · Computer Fraud and Security · 1 citations

    With the advancement of information technology, data security and user privacy protection have become paramount. To achieve efficient privacy protection in a federated learning environment, a differential privacy algorithm is designed using the eXtreme Gradient Boosting (XGBoost) algorithm. This algorithm optimizes the privacy protection process by applying differential privacy to the optimal segmentation point in a weak classifier. Additionally, to address the multi-party collaboration challenge in federated learning, a differential privacy construction scheme based on multi-party collaboration is proposed. The results indicate that the running times of differential privacy algorithms based

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21. Privacy-preserving federated learning with differential privacy for healthcare AI: a convergence and utility analysis

    Aruna Pavate · 2025 · Journal of Artificial Intelligence Machine Learning and Neural Network · 1 citations

    Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without sharing raw patient data, offering a paradigm shift for privacy-sensitive medical AI. However, FL remains vulnerable to gradient inversion attacks and model poisoning, necessitating formal privacy guarantees. This paper presents a comprehensive analysis of Differential Privacy (DP)-augmented Federated Learning for healthcare AI applications, specifically Electronic Health Record (EHR) classification. We evaluate three aggregation strategies FedAvg, FedProx, and the proposed FedNova-DP across simulated environments with 10, 25, and 50 heterogeneous clients under both IID and non-IID

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22. A federated learning privacy preserving scheme for infrastructure-less vehicular networks

    Xiangyang Luo, Xing-Xing Chen, Yu-Qian Ma, et al. · 2025 · SCIENTIA SINICA Informationis

    Vehicular networks face increasingly severe privacy leakage threats. However, current research schemes predominantly rely on the functional integrity of roadside infrastructure. This dependence makes them vulnerable to multiple security risks, such as data leakage, authentication failures, and internal malicious attacks, in infrastructure-less scenarios like damaged roadside units. To address these challenges, this manuscript proposes a blockchain-based federated learning privacy preserving scheme for infrastructure-less vehicular networks, named BFL-P3S. First, we design a dynamic group member management mechanism that leverages blockchain’s decentralization, tamper-proof, and traceability

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23. Adaptive Secure Federated Learning Privacy-Preserving Framework for Healthcare Ecosystem

    Amit Prasad, G. Manjula, A. M., et al. · 2026 · 2026 4th International Conference on Integrated Circuits and Communication Systems (ICICACS)

    From the past few years, Federated Learning (FL) has emerged as one of the promising paradigms in healthcare data analytics to enable collaborative model training for preserving privacy in various distributed institutions. However, conventional models struggle from challenges such as weak privacy, communication overhead, inaccurate aggregate strategies and lack of client reliability assessment. To overcome these challenges, this research proposed an Adaptive Secure Federated Learning (ASFL) which is integrated with limited access encryption algorithm incorporating FL (LEAF) framework. Firstly, data was collected from a publicly available brain tumor Magnetic Resonance Imaging (MRI) repositor

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24. Federated Learning Privacy-Preserving Approach for Consumer-Centric Multimodal Healthcare Data

    N. Singh, Nisha Singhal, Kalpna Jain, et al. · 2026 · IEEE Transactions on Consumer Electronics

    Electronic Health Records (EHR) data have become a valuable resource for biomedical research. By leveraging federated learning (FL), the potential of EHR data can be used without compromising privacy. However, most existing studies focus on privacy protection methods for device-centric distributed healthcare EHRs. The diverse modalities of EHR, such as consumer-centric clinical notes, lab results, and imaging data, limit the unimodal ability to capture complex relationships. FL models designed for unimodal data struggle to integrate these heterogeneous sources, resulting in suboptimal performance and increased risk of privacy breaches. This paper proposes a FL privacy protection method for m

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