Federated Learning a Collaborative Machine Learning Across Countries with Data Privacy

Abstract

With growing importance of data in shaping policies, economic strategies, and healthcare systems, securing citizens data has become a critical issue for national governments. At the same time, the potential benefits of large-scale collaborative machine learning (ML) across countries are undeniable. Federated learning (FL) offers a unique solution to this dilemma by enabling the training of AI models across decentralized data sets without requiring data to be shared. This paper explores how different countries can use federated learning to contribute to collaborative machine learning while ensuring national data security. We examine the privacy-preserving mechanisms in FL, the technical challenges, and propose a framework for cross-country collaboration on a global scale..