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. Advances and Open Problems in Federated Learning

   Peter Kairouz, H. Brendan McMahan, Brendan Avent, et al. · 2020 · Foundations and Trends® in Machine Learning · 4,919 citations

   Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges.

2. Federated Learning: Challenges, Methods, and Future Directions

   Tian Li, Anit Kumar Sahu, Ameet Talwalkar, et al. · 2020 · IEEE Signal Processing Magazine · 4,729 citations

   Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.

3. Federated Learning With Differential Privacy: Algorithms and Performance Analysis

   Kang Wei, Jun Li, Ming Ding, et al. · 2020 · IEEE Transactions on Information Forensics and Security · 2,253 citations

   Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting diff

4. Secure, privacy-preserving and federated machine learning in medical imaging

   Georgios Kaissis, Marcus R. Makowski, Daniel Rückert, et al. · 2020 · Nature Machine Intelligence · 1,369 citations

5. Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT

   Yunlong Lu, Xiaohong Huang, Yueyue Dai, et al. · 2019 · IEEE Transactions on Industrial Informatics · 1,229 citations

   The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preser

6. A Hybrid Approach to Privacy-Preserving Federated Learning

   Stacey Truex, Nathalie Baracaldo, Ali Anwar, et al. · 2019 · 932 citations

   Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively

7. Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

   Yi Liu, James J. Q. Yu, Jiawen Kang, et al. · 2020 · IEEE Internet of Things Journal · 680 citations

   Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit ne

8. A Comprehensive Survey of Privacy-preserving Federated Learning

   Xuefei Yin, Yanming Zhu, Jiankun Hu · 2021 · ACM Computing Surveys · 601 citations

   The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research direc

9. Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

   Yang Zhao, Jun Zhao, Linshan Jiang, et al. · 2020 · IEEE Internet of Things Journal · 576 citations

   Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data. Then, manufacturers can predict customers’ requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile-edge computing (MEC) server. Customers collect data from variou

10. Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

    Haokun Fang, Quan Qian · 2021 · Future Internet · 401 citations

    Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structu

11. Privacy‐preserving federated learning based on multi‐key homomorphic encryption

    Jing Ma, Si‐Ahmed Naas, Stephan Sigg, et al. · 2022 · International Journal of Intelligent Systems · 394 citations

    With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all

12. Privacy-preserving blockchain-based federated learning for traffic flow prediction

    Yuanhang Qi, M. Shamim Hossain, Jiangtian Nie, et al. · 2020 · Future Generation Computer Systems · 333 citations

13. Improving LoRA in Privacy-preserving Federated Learning

    Youbang Sun, Zitao Li, Yaliang Li, et al. · 2024 · ArXiv · 201 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

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

    Rahul Haripriya, Nilay Khare, Manish Pandey · 2025 · Scientific Reports · 94 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

15. 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 · 67 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

16. 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 · 61 citations

    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

17. 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 · 50 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

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

    Nikolas Koutsoubis, A. Waqas, Yasin Yilmaz, et al. · 2025 · Radiology. Artificial intelligence · 47 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

19. 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 · 43 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

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

    Yange Chen, Suyu He, Baocang Wang, et al. · 2025 · IEEE Transactions on Mobile Computing · 40 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

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

    Milad Rahmati, Antonino Pagano · 2025 · Informatics · 40 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,

22. Ubiquitous intelligent federated learning privacy-preserving scheme under edge computing

    Dong-fen Li, Jinshan Lai, Ruijin Wang, et al. · 2023 · Future Gener. Comput. Syst. · 33 citations

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

    Jie Huang, Cheng Xu, Zhaohua Ji, et al. · 2022 · Security and Communication Networks · 27 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

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

    Bowen Zhao, Ximeng Liu, Wei-neng Chen · 2021 · ArXiv · 24 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