FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds…

• NN
  National Natural Science Foundation of China

  Award: 61972383
• BM
  Beijing Municipal Science and Technology Commission

  Award: Z171100000117017