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. 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 · 36 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

   Save this paper

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

   Dongfen Li, Jinshan Lai, Ruijin Wang, et al. · 2023 · Future Generation Computer Systems · 36 citations

   Save this paper

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

   Bowen Zhao, Ximeng Liu, Wei–Neng Chen · 2021 · arXiv (Cornell University) · 17 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

   Save this paper

4. Research on the Security Technology of Federated Learning Privacy Preserving

   Mao Juan, Chunjie Cao, Longjuan Wang, et al. · 2021 · Journal of Physics Conference Series · 9 citations

   Abstract With the emergence of data islands and the popular awareness of privacy, federated learning, as an emerging data sharing and exchange model, can realize multi-party collaboration under the premise of protecting data privacy and security because the data distributed in multiple devices cannot be sent locally. To achieve benefits for all parties involved, it has been widely used in many fields such as finance, medical care, and education. However, FL also has various security and privacy issues. Starting from the overview of federated learning, this article describes in detail the threat model and existing security issues, including replay attacks, poisoning attacks, reasoning attacks

   Save this paper

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

   Deling Huang, Yuqiao Yang, Yifan Huang · 2023 · 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

   Save this paper

6. 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

   Save this paper

7. Federated Learning Privacy-preserving Method Based on Bregman Optimization

   Gengming Zhu, Jiyong Zhang, Shaobo Zhang, et al. · 2023 · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · 2 citations

   Federated learning has received a lot of attention for its ability to solve the data silo problem, but it is also limited by the problem of data heterogeneity and privacy. Non-Independent Identical Distribution (Non-I.I.D) data leads to performance degradation of federation models, and privacy problem have been studied as a hot topic in the field of federated learning. However, current research rarely considers non-I.I.D data and privacy simultaneously. In this paper, we propose a federated learning scheme based on Bregman and differential privacy (FLBDP). Our approach adopts Bregman distance for personalized model training, which aims to control the difference between local model and global

   Save this paper

8. Federated Learning for Privacy-Preserving AI

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

   Save this paper

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

   Amit Chouhan · 2023 · 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 the pr

   Save this paper

10. 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

    Save this paper

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

    Favour . C. Ezeugboaja · 2025 · Frontiers in Emerging Artificial Intelligence and Machine Learning · 1 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

    Save this paper

12. 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

    Save this paper

13. Federated Learning: Privacy-Preserving Data Science

    Micheal Mithra M., Nattar Devi C., V. Preethika, et al. · 2024 · International Journal of Recent Engineering Science

    A new paradigm in machine learning called federated learning allows for decentralized data processing. At the same time, it protects users’ privacy. Centralized data learning reduces the risk of data breaches and illegal access. The model can study multiple devices or data assets without sending raw logs. The basic idea of federated consciousness, its structure, and the special algorithms used to guarantee a powerful version of its education are tested in this e-book and packages that travel in sectors such as healthcare, banking, and equipment. Already a genius, We also examine challenges related to the differences noted. Model convergence and efficiency of verbal exchanges: this study aims

    Save this paper

14. Federated Learning: Privacy-Preserving Distributed Training

    Kochumol Abraham · 2026 · Zenodo (CERN European Organization for Nuclear Research)

    Federated learning enables collaborative machine learning training across distributed devices without centralizing raw data. This paper examines privacy-preserving federated learning at scale using differential privacy and secure multi-party computation (MPC). We analyze the Federated Averaging (FedAvg) algorithm and its variants including FedProx, FedNova, and Scaffold for non-IID data distributions. Differential privacy mechanisms add calibrated noise to gradients, providing formal privacy guarantees with (ε,δ)-differential privacy where typical deployments use ε=2-8. Secure aggregation through MPC protocols enables encrypted gradient aggregation without revealing individual updates. We ev

    Save this paper

15. A Review Of Federated Learning: Privacy-Preserving Machine Learning

    Rathod Neha, Mojidra kirtika, khandhediya Isha, et al. · 2026 · Zenodo (CERN European Organization for Nuclear Research)

    Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, an

    Save this paper

16. Federated Learning Privacy-preserving Scheme Based on DMK-CKKS

    Yang Zhao, Hexiang Li, Wenhao Wang, et al. · 2025

    This paper proposes a privacy-preserving federated learning (FL) framework by intorducing an enhanced multi-key homomorphic encryption scheme based on MK-CKKS. To address the vulnerabilities of original MK-CKKS against semi-honest participants and server collusion attacks in FL, we redesign the key architecture by introducing an aggregated public key derived from all participants' private keys, and propose the improved Decentralized MK-CKKS(DMK-CKKS) algorithm. This modification eliminates single-point decryption risks while reducing communication rounds through merged aggregation and collaborative decryption phases. The proposed framework ensures computational security under the Ring Learni

    Save this paper

17. Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems

    Ahmed Gheni Dawood, Ekhlas Muthanna Turki · 2026 · JOINCS (Journal of Informatics, Network, and Computer Science)

    Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based and regulated environment with even stricter requirements from regulations like GDPR, HIPAA, CCPA and future laws on data sovereignty. This paper analyzed FL in depth. It described foundational concepts, architectural approaches, algorithmic approaches, real-world and practical applications and challenges in distributed systems. Key issues such as communication overhead, data heterogeneity, security risks, fairness, scalability, energy

    Save this paper

18. 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

    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

    Save this paper

19. Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments

    Zayyanu Yunusa · 2025 · Journal of Artificial Intelligence Machine Learning and Neural Network

    In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human progress and well-being. Drawing from historical perspectives, we trace the evolution of AI and its transformative journey. Our research aims to comprehensively analyze approaches for fostering responsible AI growth while mitigating potential hazards. By illuminating both the promise and perils of AI, this study contributes to informed decision-making in the unfolding AL era. This research explores the innovative utilization of AI technolog

    Save this paper

20. Beyond the Hype: How Federated Learning and Privacy-Preserving AI Are Reshaping Clinical Decision-Making in 2026

    Qin Long, Omid Panahi · 2026 · Medical & Clinical Research

    The integration of artificial intelligence into clinical decision-making has long been constrained by a fundamental tension: high-performing models require vast, diverse datasets, yet healthcare data is siloed across institutions and protected by stringent privacy regulations. In 2026, this tension is being resolved through the maturation of federated learning (FL) and privacy-preserving AI technologies. This article examines how these decentralized approaches are moving from research prototypes to clinical reality. We analyze three key developments: (1) the emergence of FL as a practical framework for multi-institutional collaboration in oncology and pathology, enabling model training acros

    Save this paper