Federated transfer learning for disaster classification in social computing networks
Zehui Zhang, Ningxin He, Dongyu Li, Hang Gao, Tiegang Gao, Chuan Zhou
Abstract
Social media analytics have played an important role in disaster identification. Recent advances in deep learning (DL) technologies have been applied to design disaster classification models. However, the DL-based models are hindered by insufficient training samples, because data collection and labeling are very expensive and time-consuming. To solve this issue, a privacy-preserving federated transfer learning approach for disaster classification (FedTL) is proposed, which can allow distributed social computing nodes to collaboratively train a comprehensive model. In the FedTL, Paillier homomorphic encryption method is used to protect the social computing nodes’ data privacy. In particular, the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system. The FedTL is verified by a real disaster image dataset collected from social networks. Theoretical analyses and experiment results show that the FedTL is effective, secure, efficient. In addition, the FedTL is highly extensible and can be easily applied in other transfer learning models.