PROFL: A Privacy-Preserving Federated Learning Method with Stringent
  Defense Against Poisoning Attacks
Yisheng Zhong Li-Ping Wang
Federated Learning (FL) faces two major issues: privacy leakage and poisoning
attacks, which may seriously undermine the reliability and security of the
system. Overcoming them simultaneously poses a great challenge. This is because
privacy protection policies prohibit access to users' local gradients to avoid
privacy leakage, while Byzantine-robust methods necessitate access to these
gradients to defend against poisoning attacks. To address these problems, we
propose a novel privacy-preserving Byzantine-robust FL framework PROFL. PROFL
is based on the two-trapdoor additional homomorphic encryption algorithm and
blinding techniques to ensure the data privacy of the entire FL process. During
the defense process, PROFL first utilize secure Multi-Krum algorithm to remove
malicious gradients at the user level. Then, according to the Pauta criterion,
we innovatively propose a statistic-based privacy-preserving defense algorithm
to eliminate outlier interference at the feature level and resist impersonation
poisoning attacks with stronger concealment. Detailed theoretical analysis
proves the security and efficiency of the proposed method. We conducted
extensive experiments on two benchmark datasets, and PROFL improved accuracy by
39% to 75% across different attack settings compared to similar
privacy-preserving robust methods, demonstrating its significant advantage in
robustness.