VFEFL: Privacy-preserving federated learning against malicious clients via verifiable functional encryption

Summary

The paper proposes VFEFL, a privacy-preserving federated learning scheme that encrypts local models with a verifiable functional encryption scheme. It aims to protect local model privacy and defend against malicious clients who upload poisoned models or wrong ciphertexts or decryption keys, within the basic one-server, multiple-client architecture. The authors report that prototype experiments show it resists targeted and untargeted poisoning attacks while preserving model privacy.

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