Cover Image

Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Minjie, Han, Jinguang, Meng, Weizhi
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
by: Cai, Nina, et al.
Published: (2025)

Flexible Threshold Multi-client Functional Encryption for Inner Product in Federated Learning
by: Zhang, Ruyuan, et al.
Published: (2025)

Privacy-Preserving Traceable Functional Encryption for Inner Product
by: Qiu, Muyao, et al.
Published: (2024)

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
by: Xu, Runhua, et al.
Published: (2025)

A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
by: Negoya, Yusaku, et al.
Published: (2025)

FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
by: Rahulamathavan, Yogachandran, et al.
Published: (2023)

FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning
by: Gu, Chen, et al.
Published: (2025)

Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning
by: Zhang, Ruyuan, et al.
Published: (2024)

Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption
by: Ye, Huanyi, et al.
Published: (2026)

Privacy-Preserving Diffusion Model Using Homomorphic Encryption
by: Chen, Yaojian, et al.
Published: (2024)

Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption
by: Wakiyama, Koki, et al.
Published: (2025)

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
by: Piran, Fardin Jalil, et al.
Published: (2025)

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
by: Yadav, Sushilkumar, et al.
Published: (2024)

Design and Optimization of Cloud Native Homomorphic Encryption Workflows for Privacy-Preserving ML Inference
by: Bollikonda, Tejaswini
Published: (2025)

Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
by: Wang, Taotao, et al.
Published: (2025)

Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2025)

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
by: Gu, Hanlin, et al.
Published: (2024)

Robust Client-Server Watermarking for Split Federated Learning
by: Tang, Jiaxiong, et al.
Published: (2025)

MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare
by: Yesmin, Farjana
Published: (2025)

FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
by: Ye, Pukang, et al.
Published: (2025)

Privacy-Preserving in Blockchain-based Federated Learning Systems
by: M., Sameera K., et al.
Published: (2024)

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
by: Scheliga, Daniel, et al.
Published: (2023)

Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
by: Chahoud, Mario, et al.
Published: (2024)

Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks
by: Ahmed, Ansar
Published: (2025)

Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption
by: Chowdhury, Moontaha Nishat, et al.
Published: (2025)

Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption
by: Marques, Alexandre, et al.
Published: (2026)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
by: Li, Yuecheng, et al.
Published: (2024)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
by: Xu, Yukai, et al.
Published: (2024)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
by: Dong, Wenhan, et al.
Published: (2024)

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning
by: Wu, Jiahui, et al.
Published: (2025)

Secure and Privacy-Preserving Vertical Federated Learning
by: Jin, Shan, et al.
Published: (2026)

Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning
by: Ghavamipour, Ali Reza, et al.
Published: (2024)

Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness
by: Ghavamipour, Ali Reza, et al.
Published: (2024)

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
by: Hiniduma, Kaveen, et al.
Published: (2025)

Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
by: Khalil, Mohammad, et al.
Published: (2025)

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
by: Yu, Sixing, et al.
Published: (2023)

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
by: Zeng, Wenxuan, et al.
Published: (2025)

Privacy-Preserving LLMs Routing
by: Wu, Xidong, et al.
Published: (2026)