Federated Learning Attacks
Federated learning systems face unique security challenges due to their distributed nature and limited visibility into client data and behavior.
Federated learning enables training machine learning models across distributed clients without centralizing sensitive data, making it attractive for privacy-sensitive applications. However, this distributed architecture introduces unique security vulnerabilities that differ significantly from centralized training. Attackers can exploit the federated learning process at multiple points including during local training, model update transmission, and global aggregation.
The core security challenge in federated learning is that the server has limited visibility into client behavior and cannot directly validate training data or local model updates. Malicious clients can submit poisoned model updates that corrupt the global model, perform inference attacks to extract private information from aggregated updates, or launch Byzantine attacks to disrupt the training process entirely. These attacks are particularly dangerous because they can be launched by a single compromised client and propagate their effects through the global model.
Defending federated learning systems requires a combination of robust aggregation algorithms, privacy-preserving techniques, and client validation mechanisms. Understanding these attack vectors and defense strategies is essential for deploying secure federated learning systems in production environments, especially in applications involving sensitive data such as healthcare, finance, and personal devices.
Malicious clients submit poisoned model updates to corrupt the global model
- • Targeted poisoning attacks
- • Backdoor injection
- • Model replacement attacks
Compromised nodes send arbitrary malicious updates to disrupt training
- • Random noise injection
- • Gradient flipping
- • Sybil attacks
Attackers infer private information from model updates or gradients
- • Membership inference
- • Property inference
- • Gradient inversion
Federated learning attacks can have severe consequences depending on the attacker's goals and the application domain. Understanding attack scenarios helps organizations assess risks and implement appropriate defenses.
Targeted Model Corruption
Attackers inject backdoors or cause targeted misclassifications by submitting carefully crafted model updates that influence global model behavior.
- • Can be achieved with a single malicious client
- • Effects persist across training rounds
- • Difficult to detect without robust aggregation
- • Can target specific classes or features
Privacy Inference Attacks
Attackers analyze aggregated model updates to infer private information about training data, including membership inference and property inference.
- • Gradient inversion can reconstruct training samples
- • Membership inference reveals if data was used
- • Property inference extracts sensitive attributes
- • Requires differential privacy or encryption
Effective federated learning security requires multiple defense layers addressing different attack vectors. Combining robust aggregation with privacy-preserving techniques provides comprehensive protection.
Robust Aggregation
Algorithms that identify and filter out malicious model updates during aggregation:
- • Krum: Selects updates closest to neighbors, filtering outliers
- • Trimmed Mean: Removes extreme values before averaging
- • Median: Uses median instead of mean for robustness
- • FoolsGold: Detects sybil attacks through update similarity
Privacy Protection
Techniques to prevent information leakage from model updates:
- • Differential Privacy: Adds calibrated noise to updates
- • Secure Aggregation: Multi-party computation for private aggregation
- • Homomorphic Encryption: Enables computation on encrypted updates
- • Gradient Compression: Reduces information content in updates