Federated Machine Learning

Federated machine learning (FML) is a decentralized approach to training machine learning models where data remains on local devices or servers, and only model updates – not raw data – are shared with a central system. This method enables collaborative learning across multiple sources while preserving data privacy and security. Commonly used in industries like healthcare, finance, and mobile applications, FML allows organizations to build robust AI models without exposing sensitive or proprietary data. It supports regulatory compliance and enhances trust by keeping data localized while still enabling large-scale, distributed AI development.