How Federated Learning Protects Privacy G.R. Jenkin & Associates

Large datasets have made astounding breakthroughs in machine learning possible. But oftentimes data is personal or proprietary, and not meant to be shared, making privacy a critical concern of and barrier to centralized data collection and model training. With federated learning, it's possible to collaboratively train a model with data from multiple users without any raw data leaving their devices. If we can learn from data across many sources without needing to own or collect it, imagine what opportunities that opens! Billions of connected devices -- like phones, watches, vehicles, cameras, thermostats, solar panels, telescopes -- with sensors to capture data and computational power to participate in training, could collaborate to better understand our environment and ourselves.