How are adversarial attacks mitigated in federated learning?

Adversarial attacks in federated learning are mitigated through techniques that focus on detecting malicious contributions, securing communication, and ensuring robust model aggregation. Federated learning involves multiple participants training a shared model without sharing raw data, which creates vulnerabilities. Attackers might submit manipulated model updates to degrade performance, insert backdoors, or leak sensitive information. Mitigation strategies address these risks by combining secure protocols, anomaly detection, and aggregation methods resistant to outliers.

One common approach is using robust aggregation algorithms instead of simple averaging. For example, trimmed mean removes extreme values from updates before averaging, reducing the influence of outliers. Krum selects the update closest to a majority of others, discarding suspicious ones. Another method, FoolsGold, detects sybil attacks (multiple fake participants) by identifying unusually similar update patterns. Additionally, anomaly detection techniques like clustering or statistical tests (e.g., comparing update magnitudes) flag abnormal updates. For instance, a participant submitting updates ten times larger than others might be blocked. Differential privacy can also be applied by adding controlled noise to updates, limiting an attacker’s ability to infer training data or manipulate gradients.

Secure communication protocols and verification mechanisms further reduce risks. Secure Multi-Party Computation (SMPC) ensures updates are aggregated without revealing individual contributions, preventing attackers from reverse-engineering sensitive data. Homomorphic encryption allows computations on encrypted updates, though it adds computational overhead. Some frameworks also require participants to prove they trained on valid data (e.g., via zero-knowledge proofs). For example, a medical imaging project might combine Krum aggregation with SMPC to filter malicious updates while preserving patient privacy. Developers must balance security and efficiency—overly strict detection might block legitimate updates, while heavy encryption slows training. Testing these methods in simulated adversarial environments helps tune their effectiveness.