Reducing Bias and Increasing Accuracy in AI Models

In an increasingly digitized and interconnected global economy, it is critical to unleash the power of massive quantities of data that flows between edge devices, data centers and the cloud.

Machine Learning (ML) and Artificial Intelligence (AI) are already driving trillions of dollars of economic activity in fields such as healthcare, agriculture, retail and transportation. Major industries are developing algorithms, systems and models to obtain value and insights from data.

Typical ML/AI architecture uses training data that is aggregated at a central location to develop, test and train machine models. This centralized approach brings challenges in terms of security, data sovereignty, privacy, speed, efficiency and cost.

HPE Swarm Learning, the industry’s first privacy-preserving, decentralized machine learning framework, addresses these concerns by combining a decentralized architecture with blockchain technology. Only learned insights are shared among collaborating peers, not raw data. By bringing computing close to the data, HPE Swarm Learning reduces bias and increases accuracy in models, while offering enhanced security and privacy-preserving features.

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