Threats to Federated Learning: A Survey

Abstract

With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising solution under this new reality. Existing FL protocol design has been shown to exhibit vulnerabilities which can be exploited by adversaries both within and without the system to compromise data privacy. It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation. Currently, there is no survey on this topic. In this paper, we bridge this important gap in FL literature. By providing a concise introduction to the concept of FL, and a unique taxonomy covering threat models and two major attacks on FL: 1) poisoning attacks and 2) inference attacks, this paper provides an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks, and discuss promising future research directions towards more robust privacy preservation in FL.

arXiv comment7 pages, 4 figures, 2 tables

References in corpus (11)

• Deep Learning with Differential Privacy
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
• How To Backdoor Federated Learning
• Learning Differentially Private Recurrent Language Models
• Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
• Analyzing Federated Learning through an Adversarial Lens
• iDLG: Improved Deep Leakage from Gradients
• Mitigating Sybils in Federated Learning Poisoning
• Protection Against Reconstruction and Its Applications in Private Federated Learning
• Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer

Cited by in corpus (41)

• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• From Distributed Machine Learning to Federated Learning: A Survey
• Federated Reinforcement Learning: Techniques, Applications, and Open Challenges
• Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
• HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
• Think Locally, Act Globally: Federated Learning with Local and Global Representations
• A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
• Federated Continual Learning via Knowledge Fusion: A Survey
• Privacy-Preserving Machine Learning: Methods, Challenges and Directions
• Federated Learning for Healthcare Informatics
• Local Differential Privacy and Its Applications: A Comprehensive Survey
• Label Leakage and Protection in Two-party Split Learning
• The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
• Federated Contrastive Learning for Volumetric Medical Image Segmentation
• Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
• Communication Efficiency in Federated Learning: Achievements and Challenges
• A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
• Asynchronous Federated Learning on Heterogeneous Devices: A Survey
• Natural Language Understanding with Privacy-Preserving BERT
• Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
• A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
• Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
• Local Differential Privacy based Federated Learning for Internet of Things
• Auto-weighted Robust Federated Learning with Corrupted Data Sources
• A Vertical Federated Learning Framework for Graph Convolutional Network
• A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
• You Can Backdoor Personalized Federated Learning
• Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
• A Novel Attribute Reconstruction Attack in Federated Learning
• Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks
• Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
• Collaborative Fairness in Federated Learning
• DistFL: Distribution-aware Federated Learning for Mobile Scenarios
• Federated Learning on Non-IID Data: A Survey
• FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
• Towards Bidirectional Protection in Federated Learning
• Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
• Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare
• Towards Differentially Private Text Representations
• Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization