Enhancing Cloud-Based Machine Learning Models with Federated Learning Techniques

Though cloud-based machine learning is a popular option for handling large datasets, security and privacy concerns have slowed its widespread adoption. Training machine learning models with distributed data can be done in secrecy with the help of the new method of federated learning. In this research, we look into how federated learning can improve cloud-based machine learning models’ precision. In this research, we used a real-world dataset to empirically compare and contrast traditional cloud-based machine learning techniques with federated learning models. Our findings suggest that cloud-trained machine learning models can benefit greatly from federated learning, both in terms of F1 score and accuracy. This research analyzes how several parameters, like client count and training velocity, affect the performance of federated learning models as a whole. Our research shows that federated learning may significantly improve cloud-based machine learning models’ accuracy while also safeguarding users’ personal information and preserving the data’s integrity.