A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective

Abstract

This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications.

https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

References in corpus (19)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Score-Based Generative Modeling through Stochastic Differential Equations
• iDLG: Improved Deep Leakage from Gradients
• Can You Really Backdoor Federated Learning?
• Threats to Federated Learning: A Survey
• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
• DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection
• Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
• Efficient privacy preservation of big data for accurate data mining
• Efficient Parallel Methods for Deep Reinforcement Learning
• Free-riders in Federated Learning: Attacks and Defenses
• Mitigating Backdoor Attacks in Federated Learning
• Federated Learning Meets Natural Language Processing: A Survey
• Relational Multi-Task Learning: Modeling Relations between Data and Tasks
• Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
• Learning to Backdoor Federated Learning
• Local Environment Poisoning Attacks on Federated Reinforcement Learning
• Gradient Leakage Defense with Key-Lock Module for Federated Learning

Cited by in corpus (2)

• Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
• Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning