A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus

Federated learning has been widely studied and applied to various scenarios, such as financial credit, medical identification, and so on. Under these settings, federated learning protects users from exposing their private data, while cooperatively training a shared machine learning algorithm model (i.e., the global model) for a variety of realworld applications. The only data exchanged is the gradient of the model or the updated model (i.e., the local model update). However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack on the global model or user privacy data. To address these security issues, we propose a decentralized…

• NN
  National Natural Science Foundation of China

  Award: 11801595,61722214
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2018A030310076,2019A1515011043
• NK
  National Key Research and Development Program of China

  Award: 2016YFB1000101