Federated Machine Learning (FedML) is an innovative approach that allows organizations with smaller data sets to collaboratively train sophisticated machine learning models while keeping their data decentralized and private. This technology enables multiple small-scale companies to pool their resources without compromising proprietary information, thus addressing the challenges posed by the digital divide between large and small data organizations.

FedML provides smaller companies with the opportunity to train their machine learning models on larger, decentralized data sets, which they might not have access to individually. This collaborative approach helps level the playing field, allowing these companies to develop more sophisticated AI tools and compete more effectively in their respective industries.

Access to big data is crucial for developing sophisticated and high-performing AI and machine learning models. Companies with extensive data resources, like Amazon and Google, can leverage this information to create advanced algorithms that improve customer engagement and operational efficiency. In contrast, smaller organizations often face significant challenges due to their limited data, which can hinder their ability to innovate and compete in the AI landscape.