Smart manufacturing suffers from the heterogeneity of local data distribution across parties, mutual information silos and lack of privacy protection in the process of industry chain collaboration. To address these problems, we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning. Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain. A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model, while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum, mitigating the inherent heterogeneity between local data. Our experiments are conducted on the largest domain adaptation dataset, and the results show that compared with other traditional federated domain adaptation algorithms, the algorithm we proposed trains a more accurate model, requires fewer communication rounds, makes more effective use of imbalanced data in the industrial area, and protects data privacy.

Federated learning is an emerging distributed privacy-preserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous parties.