Cover Image

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mai, Peihua, Yan, Ran, Pang, Yan
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Artificial Intelligence E.4
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedCC: Robust Federated Learning against Model Poisoning Attacks
by: Jeong, Hyejun, et al.
Published: (2022)

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
by: Mai, Peihua, et al.
Published: (2025)

ConfusionPrompt: Practical Private Inference for Online Large Language Models
by: Mai, Peihua, et al.
Published: (2023)

Split-and-Denoise: Protect large language model inference with local differential privacy
by: Mai, Peihua, et al.
Published: (2023)

Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users
by: Sultan, Nazatul Haque, et al.
Published: (2025)

Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
by: Wang, Zhibo, et al.
Published: (2024)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
by: Xie, Chulin, et al.
Published: (2022)

GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
by: Talpur, Anum, et al.
Published: (2022)

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
by: M., Sameera K., et al.
Published: (2025)

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
by: Kumar, K Naveen, et al.
Published: (2024)

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
by: Carney, Jane, et al.
Published: (2025)

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
by: Xu, Runhua, et al.
Published: (2025)

SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning
by: Xu, Weida, et al.
Published: (2024)

Vertical Federated Graph Neural Network for Recommender System
by: Mai, Peihua, et al.
Published: (2023)

Fluent: Round-efficient Secure Aggregation for Private Federated Learning
by: Li, Xincheng, et al.
Published: (2024)

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

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
by: Han, Shanshan, et al.
Published: (2023)

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
by: Zhang, Heyi, et al.
Published: (2025)

A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning
by: Yu, Yinbo, et al.
Published: (2024)

Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
by: Wulnye, Fortunatus Aabangbio, et al.
Published: (2026)

Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
by: Taiello, Riccardo, et al.
Published: (2024)

Model Inversion Attack against Federated Unlearning
by: Zhou, Lei, et al.
Published: (2025)

Universal Black-Box Reward Poisoning Attack against Offline Reinforcement Learning
by: Xu, Yinglun, et al.
Published: (2024)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

Stateless and Secure Delivery versus Payment across Blockchains
by: Fries, Christian P., et al.
Published: (2023)

Defending Against Beta Poisoning Attacks in Machine Learning Models
by: Gulciftci, Nilufer, et al.
Published: (2025)

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
by: Mestari, Soumia Zohra El, 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)

DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification
by: Liu, Sheng, et al.
Published: (2025)

Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
by: Alharbi, Ebtisaam, et al.
Published: (2025)

On The Dangers of Poisoned LLMs In Security Automation
by: Karlsen, Patrick, et al.
Published: (2025)

CSC: Turning the Adversary's Poison against Itself
by: Shi, Yuchen, et al.
Published: (2026)

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
by: Li, Kai, et al.
Published: (2024)

FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
by: Campos, Enrique Mármol, et al.
Published: (2024)

Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
by: Zou, Wei, et al.
Published: (2026)

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
by: Singampalli, Akhil, et al.
Published: (2024)

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
by: Meerza, Syed Irfan Ali, et al.
Published: (2024)

Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language Models
by: Truong, Vu Tuan, et al.
Published: (2026)

VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning
by: Tan, Juntao, et al.
Published: (2025)

Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs
by: Rastogi, Nidhi, et al.
Published: (2025)