FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks
This addresses security and reliability issues in federated learning for safety-critical domains like autonomous vehicles and healthcare, offering a practical solution that is incremental over existing defenses.
The paper tackles the problem of securing federated learning against poisoning attacks by proposing FLShield, a framework that validates local models using benign participant data, achieving effective defense without requiring server access to clean datasets.
Federated learning (FL) is revolutionizing how we learn from data. With its growing popularity, it is now being used in many safety-critical domains such as autonomous vehicles and healthcare. Since thousands of participants can contribute in this collaborative setting, it is, however, challenging to ensure security and reliability of such systems. This highlights the need to design FL systems that are secure and robust against malicious participants' actions while also ensuring high utility, privacy of local data, and efficiency. In this paper, we propose a novel FL framework dubbed as FLShield that utilizes benign data from FL participants to validate the local models before taking them into account for generating the global model. This is in stark contrast with existing defenses relying on server's access to clean datasets -- an assumption often impractical in real-life scenarios and conflicting with the fundamentals of FL. We conduct extensive experiments to evaluate our FLShield framework in different settings and demonstrate its effectiveness in thwarting various types of poisoning and backdoor attacks including a defense-aware one. FLShield also preserves privacy of local data against gradient inversion attacks.