Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Nowadays, the internet of things (IoT) is used to generate data in several application domains. A logistic regression, which a standard machine learning algorithm with wide range, built on such data. Nevertheless, building powerful and effective regression model requires large amounts Thus, collaboration between multiple IoT participants has often been go-to approach. However, privacy concerns poor quality are two challenges that threaten success setting. Several studies have proposed different methods address concern but best our knowledge, little attention paid towards addressing problems multi-party model. this study, we propose privacy-preserving framework filtering for contributors both problems. Specifically, new metric gradient similarity distributed setting employ filter out parameters from To solve challenge, homomorphic encryption. Theoretical analysis experimental evaluations using real-world datasets demonstrate robust against