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Papers on “federated learning privacy preserving”

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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 cites

   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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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 cites

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

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

   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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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 cites

   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

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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 cites

   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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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 cites

   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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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 cites

   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

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8. Federated Learning for Privacy-Preserving AI

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

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