Federated Machine Learning (FedML) is an innovative approach that enables smaller organizations to train sophisticated machine learning models using decentralized data. This technology allows companies with limited data sets to collaborate while maintaining the privacy of their proprietary information, thus helping to bridge the digital divide between large and small data organizations.

FedML helps smaller companies by allowing them to pool their data in a privacy-preserving manner, enabling them to train AI models that would otherwise be out of reach due to their limited data. This collaborative approach not only enhances the performance of AI tools but also allows smaller firms to compete more effectively with larger companies that have access to vast amounts of data.

The implementation of Federated Machine Learning in industries such as healthcare, manufacturing, and construction can significantly reshape how these sectors leverage AI. By enabling smaller companies to access advanced machine learning capabilities, FedML can lead to improved decision-making, enhanced operational efficiency, and ultimately, a more equitable competitive landscape across various industries.