An adaptive robust defending algorithm against backdoor attacks in federated learning

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Learning to Detect Malicious Clients for Robust Federated LearningPreprint

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DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

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Byzantine-Robust Federated Machine Learning through Adaptive Model AveragingPreprint

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Provably Secure Federated Learning against Malicious Clients

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Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentPreprint

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Abnormal client behavior detection in federated learning

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CRFL: Certifiably Robust Federated Learning against Backdoor AttacksPreprint

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Federated Learning With Soft Clustering

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Detection and Mitigation of Label-Flipping Attacks in Federated Learning Systems with KPCA and K-Means

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FLGUARD: Secure and Private Federated Learning

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Zeno: Byzantine-suspicious stochastic gradient descentPreprint

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Romoa: Robust Model Aggregation for the Resistance of Federated Learning to Model Poisoning Attacks

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Resisting Distributed Backdoor Attacks in Federated Learning: A Dynamic Norm Clipping Approach

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FLVoogd: Robust And Privacy Preserving Federated LearningPreprint

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Meta Federated LearningPreprint

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Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey

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SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments

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RoPE: Defending against backdoor attacks in federated learning systems

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Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data

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GHPPFL: A Privacy Preserving Federated Learning Based on Gradient Compression and Homomorphic Encryption in Consumer App Security

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MITDBA: Mitigating Dynamic Backdoor Attacks in Federated Learning for IoT Applications

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MoCC-BD-FID: Multi-Objective Clustering Combination-Based Backdoor Defense for Federated Intrusion Detection of Industrial Control Systems

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FedQmc: A Federated Learning Backdoor Defense Method Based on Quantum State Encoding and Multiscale Aggregation in Industrial Internet of Things

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Two-Dimensional Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks

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PerVK: A Robust Personalized Federated Framework to Defend Against Backdoor Attacks for IoT Applications

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Breaking classification Algorithms: A Black-Box Adversarial Attack for Data Poisoning

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Dual-Layered Model Protection Scheme Against Backdoor Attacks in Fog Computing-Based Federated Learning

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Improved Federated Learning Model Against Poisoning Attacks by Using Iterative Blockchain Validators

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FedPCAN: Robust mitigation of dynamic black-box backdoor attacks in federated learning

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FedRISC-IIoT: A federated learning poisoning defense framework combining reputation-driven client selection and layered robust aggregation for industrial IoT

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Federated Learning Backdoor Attack Using Dynamic Parameter Adjustment and Collaborative Trigger Optimization

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EvoACC-BD-MIDS: Evolutionary Activation Clustering Combination-Based Backdoor Defense for Multi-Class Intrusion Detection Systems in Smart Grids

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Defense against backdoor attack in federated learning

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Defense against backdoor attacks in federated learning with robust adaptive learning rates

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RFLDefense: robust federated learning defense against targeted attacks

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ADFL: Defending backdoor attacks in federated learning via adversarial distillation

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RFLBAT: A Robust Federated Learning Algorithm against Backdoor AttackPreprint

Wang, Y., Zhai, D., Zhan, Y., & Xia, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.03772 · Cited by 11 · see citing works