Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges

Advances and Open Problems in Federated Learning

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Analyzing and Improving the Image Quality of StyleGAN

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Membership Inference Attacks Against Machine Learning Models

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The Algorithmic Foundations of Differential Privacy

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ZOO

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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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Exploiting Unintended Feature Leakage in Collaborative Learning

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A survey on security and privacy of federated learning

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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

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Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

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Local Privacy and Statistical Minimax Rates

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

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Robust Aggregation for Federated Learning

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Data Poisoning Attacks Against Federated Learning Systems

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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

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Can machine learning be secure?

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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

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Federated learning for predicting clinical outcomes in patients with COVID-19

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Backdoor Learning: A Survey

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Federated learning meets blockchain in edge computing: Opportunities and challenges

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Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

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Model Pruning Enables Efficient Federated Learning on Edge Devices

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Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

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Privacy and Robustness in Federated Learning: Attacks and Defenses

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Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion

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Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

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See through Gradients: Image Batch Recovery via GradInversion

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Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations

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Local Differential Privacy-Based Federated Learning for Internet of Things

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LDP-Fed: federated learning with local differential privacy

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RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

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Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning

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Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

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User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

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Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

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Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

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PPFL

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Blockchain-enabled Federated Learning: A Survey

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Understanding Distributed Poisoning Attack in Federated Learning

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Outlier Detection for Time Series with Recurrent Autoencoder Ensembles

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Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

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Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks

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Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT

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Towards Efficient and Privacy-Preserving Federated Deep Learning

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Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-Based IIoT Networks

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The Impact of Adversarial Attacks on Federated Learning: A Survey

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Decentralized Federated Learning: A Survey on Security and Privacy

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Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

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A training-integrity privacy-preserving federated learning scheme with trusted execution environment

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On the Byzantine Robustness of Clustered Federated Learning

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Decentralized Federated Learning With Unreliable Communications

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Deep Model Poisoning Attack on Federated Learning

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Privacy-Preserving Deep Learning: Revisited and Enhanced

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Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

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Decentralized federated learning through proxy model sharing

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An overview of implementing security and privacy in federated learning

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

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Federated learning in smart cities: Privacy and security survey

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Overcoming Noisy and Irrelevant Data in Federated Learning

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UAV-Enabled Covert Federated Learning

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Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage

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Do Gradient Inversion Attacks Make Federated Learning Unsafe?

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Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural IoT Networks

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Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

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A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

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Differentially Private Federated Learning with Local Regularization and Sparsification

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Blockchain-Based Federated Learning Technique for Privacy Preservation and Security of Smart Electronic Health Records

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Two-Stream Federated Learning: Reduce the Communication Costs

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Federated Model Distillation with Noise-Free Differential Privacy

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Agricultural data privacy and federated learning: A review of challenges and opportunities

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Zero Knowledge Clustering Based Adversarial Mitigation in Heterogeneous Federated Learning

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Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions

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Privacy-preserving in Blockchain-based Federated Learning systems

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Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning

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On defending against label flipping attacks on malware detection systems

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PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems

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Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

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Federated Learning Empowered Recommendation Model for Financial Consumer Services

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MaskCrypt: Federated Learning With Selective Homomorphic Encryption

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A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

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A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy

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Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

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A federated machine learning approach for order-level risk prediction in Supply Chain Financing

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PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network

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Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks

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A survey of security threats in federated learning

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Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders

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SVeriFL: Successive verifiable federated learning with privacy-preserving

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Advances in Robust Federated Learning: A Survey With Heterogeneity Considerations

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Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

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MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning

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Attack Power System State Estimation by Implicitly Learning the Underlying Models

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Federated Data Cleaning: Collaborative and Privacy-Preserving Data Cleaning for Edge Intelligence

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Backdoor attack and defense in federated generative adversarial network-based medical image synthesis

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Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space

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Advancements in Federated Learning: Models, Methods, and Privacy

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Exploring Adversarial Attacks in Federated Learning for Medical Imaging

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Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning

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FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

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Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

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Label noise analysis meets adversarial training: A defense against label poisoning in federated learning

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Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning

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Bandit-based data poisoning attack against federated learning for autonomous driving models

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Model Poisoning Defense on Federated Learning: A Validation Based Approach

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Practical Feature Inference Attack in Vertical Federated Learning During Prediction in Artificial Internet of Things

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A Triggerless Backdoor Attack and Defense Mechanism for Intelligent Task Offloading in Multi-UAV Systems

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Quality Inference in Federated Learning With Secure Aggregation

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Collusive Backdoor Attacks in Federated Learning Frameworks for IoT Systems

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Enhancing federated learning robustness in adversarial environment through clustering Non-IID features

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FL2DP: Privacy-Preserving Federated Learning Via Differential Privacy for Artificial IoT

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Fair detection of poisoning attacks in federated learning on non-i.i.d. data

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Defending against Backdoors in Federated Learning with Robust Learning Rate

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Data Encoding for Byzantine-Resilient Distributed Optimization

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Secure and efficient multi-key aggregation for federated learning

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Shielding Federated Learning: A New Attack Approach and Its Defense

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A Huber Loss Minimization Approach to Byzantine Robust Federated Learning

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Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

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martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

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Towards the Robustness of Differentially Private Federated Learning

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Distribution-Balanced Federated Learning for Fault Identification of Power Lines

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Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

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Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning

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Improve individual fairness in federated learning via adversarial training

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Learning to Generate Image Embeddings with User-Level Differential Privacy

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Towards Practical Backdoor Attacks on Federated Learning Systems

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Secure Federated Learning for Cloud-Fog Automation: Vulnerabilities, Challenges, Solutions, and Future Directions

Zhang, Z., Wu, L., Jin, J., Wang, E., Liu, B., & Han, Q. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3528569 · Cited by 20 · see citing works

MODA: Model Ownership Deprivation Attack in Asynchronous Federated Learning

Zhang, X., Lin, S., Chen, C., & Chen, X. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3348204 · Cited by 20 · see citing works

Data Poisoning Attacks With Hybrid Particle Swarm Optimization Algorithms Against Federated Learning in Connected and Autonomous Vehicles

Cui, C., Du, H., Jia, Z., Zhang, X., He, Y., & Yang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3337638 · Cited by 15 · see citing works

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

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Genuinely distributed Byzantine machine learning

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FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system

Li, X., Wang, N., Yuan, S., & Guan, Z. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103936 · Cited by 14 · see citing works

Attacking Modulation Recognition With Adversarial Federated Learning in Cognitive-Radio-Enabled IoT

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Energy-Efficient Wireless Federated Learning: A Secrecy Oriented Design via Sequential Artificial Jamming

Wang, T., Huang, N., Wu, Y., & Quek, T. Q. S. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3229277 · Cited by 12 · see citing works

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

Wang, Y., Zhai, D., Han, D., Guan, Y., & Xia, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325634 · Cited by 11 · see citing works

FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application

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Contribution-wise Byzantine-robust aggregation for Class-Balanced Federated Learning

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ZETA: ZEro-Trust Attack Framework with Split Learning for Autonomous Vehicles in 6G Networks

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A CatBoost Based Approach to Detect Label Flipping Poisoning Attack in Hardware Trojan Detection Systems

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Privacy Protection Federated Learning Framework Based on Blockchain and Committee Consensus in IoT Devices

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iDP-FL: A fine-grained and privacy-aware federated learning framework for deep neural networks

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Misbehaviour Detection for Smart Grids using a Privacy-centric and Computationally Efficient Federated Learning Approach

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GAN-Based Privacy Abuse Attack on Federated Learning in IoT Networks

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Federated Learning Security: An Incremental Distributed Attack Approach

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

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Federated RNN for Intrusion Detection System in IoT Environment Under Adversarial Attack

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A Survey on Security and Privacy in Federated Learning-Based Intrusion Detection Systems for 5G and Beyond Networks

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MT-KD: Multi-Teacher Knowledge Distillation for Privacy-Aware Unlearning in IIoT Intrusion Detection Systems

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Blockchain-enabled defenses in federated learning: A comprehensive survey

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Enhancing Federated Learning Security with a Defense Framework Against Adversarial Attacks in Privacy-Sensitive Healthcare Applications

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Review on federated optimization in multi-tier architectures

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From privacy to trust in the agentic era: a taxonomy of challenges in trustworthy federated learning through the lens of trust report 2.0

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Advancing Privacy and Accuracy With Federated Learning and Homomorphic Encryption

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Privacy‐Aware Federated Recommender Systems for IoT Healthcare: A Review of Computational Methods, Multimodality, and Optimization for Real‐Time Personalization

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Defending Against Model Inversion Attacks for Biomedical Images via Learnable Data Perturbation

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Unfederated: Open Challenges, Deployment Gaps, and Emerging Directions in Federated Learning

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A Defense Method for Mitigating Poisoning Attacks in Federated Learning Using ACGAN

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A Secure Threat Detection Framework for Federated Learning in Healthcare Environments

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A Comprehensive Literature Review of Privacy Preserving Techniques and Challenges in Decentralized Federated Learning

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Privacy-Preserving and Poisoning-Resilient Enhanced Cross-Silo Federated Learning

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Defending federated learning against adversarial attacks: A systematic literature review

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Supply chain information security sharing technology based on blockchain consensus algorithm and federated learning

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Distributed Backdoor Attack Against Knowledge Distillation-Based Federated Learning

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A review of federated learning: architectures, challenges, and targeted solutions

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FedSCOPE: A Comprehensive Evaluation Framework for Federated Learning in Human-Centered Social Computing

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A Data-Quality-based Clustering Approach for Client Selection in Federated Learning

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Inversion, Membership, and Poisoning Attacks in Federated Learning - Challenges and Defenses

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Toward Better Privacy-Protected Federated Learning for Healthcare Services in Non-IID Scenarios

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FedTriad: A “Self-Peer-Global” Trust Triangle for Federated Learning Backdoor Defense

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Gut Microbiome, Machine Learning, and Artificial Intelligence Way Forward with Precision Medicine

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Distributed Quantum Learning Over Near-Term Devices: Convergence Analysis and Security Design

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FedCleanse: A Post-Hoc Unlearning Technique for Federated Learning Against Label Flipping Attacks

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Strengthening Cyber Defense via Federated Risk-Based Adaptive Multi-factor Authentication

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Topology-Independent Learning Error of the Weighted Mean Under Label Poisoning Attacks in Heterogeneous Decentralized Learning

Peng, J., Li, W., Vlaski, S., & Ling, Q. H. (2026) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2026.3712234

Poisoning Attacks in Federated Learning: An Accountability- Oriented Survey with Centralized Learning as a Baseline

Mohammed, S., Alhadidi, D., & Ngom, A. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6040133

Federated SVDD Prototype Exchange for Decentralized Object Detection in Natural Disasters

Vlachos, E., Mygdalis, V., & Pitas, I. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630239

RAFALE: Runtime Attestation for Secure and Trustworthy Federated Learning on Edge Devices

Srivastav, P., Kaur, A., & Ghoshal, B. (2026) · Electronics · doi:10.3390/electronics15163635

A comprehensive survey on robust federated learning: From threat models to defense mechanisms

Fu, X., Yan, H., Zhou, D., Zeng, S., & Li, F. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.101041

Conformal Label-Transition Auditing Against Label-Flipping Attacks in Trust-Aware Federated Learning

Wu, C.-H., Hsu, W.-C., Lien, Y.-A., Huang, S.-H., & Chou, L.-D. (2026) · International Conference on Computational Collective Intelligence · doi:10.1109/iccci70321.2026.11666546

Diffusion to Obfuscation: Time-Adaptive Synthesized Generation Against Gradient Leakage Attacks in Federated Learning

Raswa, F. H., Lu, C.-S., & Wang, J.-C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37369-4_10

Securing Federated Learning: a comprehensive survey on poisoning attacks and defense mechanisms

Shan, X., Zhang, J., Zhang, M., Yu, H., & Yang, Z. (2026) · The Knowledge Engineering Review · doi:10.48130/ker-0026-0014

Explainable federated learning for spatio-temporal stress inference in distributed agricultural monitoring

Mallick, M. T., Banerjee, S., Ganguly, M., Kumar, A., Turdiev, J., Saha, H. N., & Chakrabarti, A. (2026) · Cogent Food & Agriculture · doi:10.1080/23311932.2026.2682021

A Cognitive, Quantum-Inspired Learning Framework for Energy-Efficient and Reliable Routing in Wireless Body Area Networks

Othman, S. B., & Saeed, N. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10665-9

A Two-Phase Federated Optimization Framework for Process Parameter Settings of Electrodeposition

Huang, D., Yan, Y., Wang, C., Zhao, X., Wu, Q., Qiu, X., & Li, Z. (2026) · Electronics · doi:10.3390/electronics15215035

Security and Privacy in Federated Learning: A Survey and Unified Taxonomy of Threat Models, Attacks, and DefensesPreprint

Fenoglio, D., Gjoreski, M., Gabrielli, E., & Langheinrich, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6771005

On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future DirectionsPreprint

Jimenez-Gutierrez, D. M., Falkouskaya, Y., Hernández-Ramos, J. L., Anagnostopoulos, A., Chatzigiannakis, I., & Vitaletti, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5561635 · Cited by 6 · see citing works

A Review on Federated Learning with a Focus on Security and Privacy

Mathew, A., & Panchami, V. (2024) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics61201.2024.10689882 · Cited by 3 · see citing works

Guarding Privacy in Federated Learning: Exploring Threat Landscapes and Countermeasures with Case Studies

Vasa, J., Thakkar, A. R., Bhavsar, D., & Patel, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-9523-9_19 · Cited by 1 · see citing works

Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

Liu, P., Xu, X., & Wang, W. (2022) · Cybersecurity · doi:10.1186/s42400-021-00105-6 · Cited by 261 · see citing works