Federated transfer learning for remaining useful life prediction in prognostics with data privacy

Abstract Collaborative model training with multiple clients is becoming an effective solution for prognostic problems, due to the scarcity of the machine run-to-failure data in the real industries. However, direct data sharing and centralized learning are usually not feasible in practice, since the private local data basically cannot be exposed to the other commercial clients. Furthermore, the machines at different clients mostly have different degradation patterns and failure modes, resulting in different data distributions. That poses great challenges for data-driven knowledge transfer across clients with data privacy. To address these issues, this paper proposes a federated transfer learning method for…

• NN
  National Natural Science Foundation of China

  Award: 52475125
• MO
  Ministry of Education of the People's Republic of China

  Award: HKZY20220429