Federated Analytics
Differential Privacy (DP) is a mathematical framework designed to provide robust privacy guarantees when releasing statistics from datasets. It ensures that the inclusion or exclusion of any single individual's data has a negligible effect on the outcome of an analysis. This article serves as an in-depth explanation of
Federated Analytics
Artificial intelligence (AI) has made significant strides across various industries, yet the challenge of accessing large, diverse datasets—particularly in sectors like healthcare—persists due to privacy concerns. Federated learning emerges as a transformative solution, enabling collaborative model training without compromising data security. What Is Federated Learning? Federated Learning (FL)