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

The paper proposes VFEFL (a privacy-preserving federated learning scheme based on verifiable functional encryption) as the solution. According to the source, VFEFL 'employ[s] a verifiable functional encryption scheme to encrypt local models in the federated learning, ensuring data privacy and correctness during encryption and decryption' and 'enables verifiable client-side aggregated weights and can be integrated into standard federated learning architectures to enhance trust.' The source states that 'experimental results demonstrate that VFEFL effectively defends against such attacks while preserving model privacy' under both targeted and untargeted poisoning attacks.