Introduction: A Glimpse into the AI Revolution
Imagine a world where your smartphone learns to predict your preferences without ever uploading your personal data to the cloud. At the same time, thousands of hospitals use AI to train global medical models collaboratively — without a single patient’s data leaving their premises. This is not some futuristic dream. It is the emerging landscape of Federated Learning (FL) and Decentralized AI, two disruptive technologies revolutionizing data privacy, scalability, and the democratization of machine learning.
In the age of growing AI, these paradigms promise to shift the focus from centralized data monopolies to a decentralized network of collaborative nodes. However, this transformation brings a series of technical, logistical, and ethical challenges. Let’s dive into how federated learning and decentralized AI are already shaping the present — and what the future holds for these exciting fields.
What is Federated Learning (FL)?
Federated Learning is a machine learning technique that allows models to be trained across multiple devices or servers without moving data to a centralized location. Instead of pooling all the data in one place, the training happens locally on different nodes, with only the model…
