Introduction To Federated Learning

Federated learning is a decentralized approach to AI training that addresses the issue of limited access to high-quality data while preserving data privacy. It allows multiple entities to collaboratively train AI models by keeping data localized and only sharing model updates. This method overcomes logistical and privacy-related challenges, making it valuable for industries like healthcare, finance, and automotive. Federated learning relies on secure aggregation techniques and handles data heterogeneity, representing a significant advancement in privacy-conscious AI development.