Federated learning is a decentralized machine learning approach that enables multiple participants to collaboratively train models while keeping their data locally. Although it offers privacy benefits by reducing the need to share raw data, it is not without vulnerabilities. One major concern is model poisoning, where a malicious participant intentionally injects erroneous data or manipulated updates to harm the overall model performance. For instance, if an attacker controls a device that contributes to the model training process, they could alter the updates in a way that represents skewed or biased information, ultimately degrading the model’s accuracy.
Another vulnerability arises from communication security. Since federated learning involves sharing model updates over potentially unsecured networks, these updates can be intercepted or tampered with. An attacker could execute a man-in-the-middle attack, where they alter the updates during transmission. For example, if a compromised server receives and modifies the updates before aggregating them, the integrity of the entire learning process could be compromised. This highlights the need for robust encryption and secure communication protocols to protect the data exchanged during training.
Lastly, there is the issue of data leakage through model inversion attacks. Even though the raw data remains local, an adversary can observe model updates and learn information about the underlying data. For example, by repeatedly querying the model and analyzing the outputs, an attacker could reconstruct sensitive data points. This vulnerability underscores the importance of implementing differential privacy techniques that introduce randomness to the model updates, thereby safeguarding individual data contributions. Developers must remain vigilant about these potential threats as they deploy federated learning solutions.