Research And Application Of Multi-source Data User Portrait Based On Federated Learnin

User profiling is an analytical tool that can deeply explore the potential value of massive data,and is widely used in scenarios such as assisted decision-making,advertising and behavior prediction.Small,medium and micro enterprises seeking cooperation on profiling due to lack of sufficient data features face problems such as difficulty in integrating heterogeneous data across regions and difficulty in ensuring data privacy and security.Federated learning is a distributed machine learning framework that can complete joint training of multiple participants without leaving the domain,which can better solve the problem of legal and compliant use of data in joint training of user portraits.Based on this,a federated learning mechanism is introduced into the traditional portrait scheme,and the privacy reinforcement scheme is further investigated by combining differential privacy and secret sharing techniques to address the privacy leakage risk of the federated learning framework,and finally the core functions of the system are designed and implemented.The details of the research are as follows:Proposing a federal learning-based design solution for user profiling from multiple sources of data.To address the problems faced by small,medium,and micro enterprises in portrait cooperation,such as the difficulty of integrating heterogeneous data resources and the difficulty of ensuring data privacy security,we propose a multisource data user portrait design scheme based on federated learning,and realize multisource data sharing by using federated learning computational mechanism and privacy intersection algorithm.The experiments show that the scheme can significantly improve the prediction accuracy and has higher privacy security and algorithm scalability compared with the local portrait scheme and the multi-source data portrait scheme.Proposing a multi-server federation learning scheme based on differential privacy and secret sharing.To address the privacy leakage risk in the federal learning framework,a multi-server federal learning scheme is proposed by combining differential privacy and secret sharing techniques,adding noise satisfying approximate differential privacy to the models trained by local users and distributing the noise-added gradients to multiple servers via a secret sharing protocol.Experiments show that the scheme has high security and the performance loss is only about 4% compared to the plaintext scheme,and the overall computational overhead is reduced by nearly 53% compared to the single-server encryption scheme.Design and implement system functions.Based on the above scheme,a federated learning-based multi-source number of user portraits system is designed and implemented to meet the needs of multi-participant joint portrait training through a joint training module and a portrait application module,while ensuring the privacy and security of user data based on security protocols.