PASS: A Parameter Audit-based Secure and Fair Federated Learning Scheme against Free-Rider Attack

Understand

Federated Learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data.

• However, traditional FL systems are vulnerable to Free-Rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems.
• The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients.
• Moreover, they aimed for Anonymous FR (AFR) attacks and lost effectiveness in resisting Selfish FR (SFR) attacks.