Federated learning introduces unique security and privacy challenges despite its decentralized approach. While it avoids sharing raw data, the process of training models across distributed devices or servers creates vulnerabilities in three key areas: model poisoning, privacy leakage, and system-level risks. Understanding these weaknesses is critical for developers designing robust federated systems.
Model Poisoning Attacks Malicious participants can manipulate the global model by submitting corrupted updates. For example, an attacker controlling even a small subset of clients could send gradients designed to skew the model’s predictions. In a federated image classifier, this might involve subtly altering updates to mislabel “stop signs” as “speed limit signs.” Such attacks can be targeted (affecting specific classes) or untargeted (general performance degradation). The aggregation server’s inability to fully validate local updates—especially when using simple averaging—amplifies this risk. Defenses like anomaly detection or robust aggregation (e.g., trimmed mean) help but require careful tuning to avoid rejecting legitimate updates from non-identical data distributions.
Privacy Leakage Even without raw data exchange, model updates can expose sensitive information. Recent research demonstrates that gradients shared during training may allow adversaries to reconstruct original training samples through techniques like gradient inversion. For instance, in a federated healthcare model, patient records might be partially reconstructed from weight updates. Additionally, membership inference attacks could determine if a specific data point was used in training. While differential privacy (DP) can mask updates with noise, implementing DP without severely degrading model accuracy remains challenging, especially for complex models.
System and Coordination Risks The federated architecture itself introduces operational vulnerabilities. A compromised central server could distribute backdoored models or manipulate aggregation rules. Communication bottlenecks between devices and servers create opportunities for man-in-the-middle attacks, particularly if encryption isn’t end-to-end. Non-IID (non-independent, identically distributed) data across clients—common in real-world scenarios like mobile keyboards—can also degrade model fairness, inadvertently creating biases against underrepresented groups. For example, a federated loan approval model trained mostly on urban users might perform poorly for rural populations. Synchronization challenges in large-scale deployments further complicate secure model versioning and update validation.
Developers must address these vulnerabilities through layered defenses: secure aggregation protocols, client authentication, and rigorous testing for data distribution skew alongside traditional ML security practices.