PPFPL: Cross-Silo Privacy-Preserving Federated Prototype Learning Against Data Poisoning Attacks

Privacy-preserving federated learning (PPFL, a method where multiple computers train AI models together while keeping their data secret) is vulnerable to data poisoning attacks (where attackers intentionally corrupt training data to sabotage the model). This paper proposes PPFPL, a framework that uses prototypes (simplified representations of model updates) and homomorphic encryption (a technique allowing calculations on encrypted data without decrypting it) to protect against poisoning attacks while maintaining privacy in distributed learning scenarios.