PBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0

In Industry 4.0, artificial intelligence (AI) has been successfully applied in scenarios, such as fault prediction, traffic analysis, and production decision making. However, due to the sensitivity and security of data, privacy regulations prohibit the transfer and exchange of industrial data between entities, resulting in training data being fragmented into data silos that limit the accuracy of AI models. FL can effectively break the data silo effect, but naive federated learning (FL) (FedAvg) is vulnerable to inference attacks from aggregators and Byzantine attacks from participants. To address these issues, we propose a privacy-preserving and Byzantine-robust federated learning scheme (PBFL) for Industry 4.0. Under the setting of an benign-majority participants, PBFL can always identify benign direction and magnitude of updates. Extensive experiments demonstrate that PBFL is more robust than state-of-the-art schemes, even with extreme proportion (49%) of malicious participants. Moreover, PBFL contains a series of well-optimized 2-party computation (2PC) protocols, causing it reduces total runtime of the unoptimized implementation by around
$3 \times \sim 4 \times $
and
$9 \times \sim 10 \times $
for 32-bit and 64-bit circuits, respectively.