Federated Learning for Heterogeneous Data Integration and Privacy Protection

Federated learning (FL) represents a promising approach that enables the collaborative training of machine learning models without compromising data privacy. This approach is particularly advantageous when handling heterogeneous data dispersed across numerous institutions or devices, as centralized data aggregation is often constrained by privacy concerns and data regulations. In order to address the challenges posed by heterogeneous data, we have devised an adaptive data integration mechanism. This mechanism maps the features of disparate data sources to a unified feature space through the use of feature alignment technology, thereby facilitating the effective fusion of data. This fusion is achieved through the application of statistical alignment and multi-perspective learning technology. Furthermore, in order to safeguard the confidentiality of data, we integrate differential privacy and homomorphic encryption techniques, thereby preventing the disclosure of information during model updates and data transfers. Furthermore, a multi-level privacy protection strategy is proposed, which employs de-identification, secure multi-party computation, and federated averaging technologies at the three stages of data preprocessing, model training, and result aggregation, respectively. This approach ensures data security and facilitates effective model updates. The experimental results demonstrate that the proposed framework exhibits enhanced model performance and robustness in comparison to traditional federated learning methods on a multitude of real-world heterogeneous datasets.