Benefits

The Brainstems' decentralized network has been engineered to revolutionize federated learning-and it is all made possible by our ecosystem of contributors.

Previously, our docs outlined the Benefits of Brainstems' Decentralized Federated Intelligence Mesh (DeFIM) technology. Below, we will discuss how these benefits are directly expressed through the design of the Brainstems ecosystem.

Why would a business use Brainstems?

We are currently performing a thorough cost savings audit comparing Decentralized Federated Learning with other AI training models. Results will be posted here!

Benefits Deep-Dive

Enhanced AI Learning

Brainstems facilitates the collaborative training of AI models by combining private datasets from various enterprises. This approach leads to the development of more robust and accurate models, as it leverages federated learning to aggregate insights from diverse sources. By participating in Brainstems, businesses can enhance their AI capabilities through access to a broader range of data and intelligence.

Cost Savings

With Brainstems, businesses can significantly reduce the financial barriers associated with AI development. Unlike traditional approaches that require hefty infrastructure setup costs, Brainstems offers a more cost-effective model where organizations only pay for the resources they use. Additionally, the decentralized nature of Brainstems eliminates the need for expensive hardware investments and ongoing maintenance costs, making advanced AI capabilities more accessible to a wider range of businesses.

Data Handling & Security

Data security is a top priority within the Brainstems ecosystem. Through state-of-the-art encryption methodologies such as homomorphic encryption and zero-knowledge proofs, Brainstems ensures that data remains encrypted throughout the process, safeguarding sensitive information from unauthorized access. Furthermore, the decentralized federated learning approach minimizes the risk of data breaches by distributing data processing across multiple nodes, reducing the likelihood of a single point of failure.

Proper data handling sits in the center of what we do. Before the start of any federated training process, Federation Partners will leverage our secure light node toolkit (hosted on enterprises' own servers) to collect, preprocess and encrypt their data. Post-encryption, data will be transferred to full computation nodes for individual training, with encrypted model weights then passed to aggregation nodes for final model creation. To ensure this process is run correctly, validation nodes apply SMPC for secure model performance assessment and computation nodes generate ZKPs for training, which are also sent to validation nodes.

Revenue Opportunities

Participating in Brainstems opens up new revenue opportunities for businesses. By contributing to the federated learning process, organizations can monetize their data more effectively and potentially access new revenue streams from subscriptions and model utilization. Additionally, the Brainstems financing model enables third-party Stewards to invest in Federated Deployments and earn returns based on the deployment's success, creating additional revenue opportunities for participants.

Scalability

Brainstems is designed to be highly scalable, allowing businesses to seamlessly expand their AI capabilities as needed. The decentralized nature of Brainstems ensures that the network can adapt and grow dynamically, accommodating the evolving needs of participants and supporting the development of new AI solutions. As the ecosystem evolves, Brainstems aims to become a permissionless environment where data and models can be freely contributed for federation, further enhancing scalability and accessibility.

Collaboration and Innovation

Brainstems fosters collaboration among businesses within and across industries by providing a platform for sharing data, expertise, and resources in a secure and trustless manner. The decentralized federated learning approach encourages innovation by allowing businesses to collectively develop and refine AI models, leveraging the diverse perspectives and insights of network participants.

Enhanced Data Governance

Brainstems facilitates better data governance practices by providing tools for data owners to maintain control over their data throughout the federated learning process. Through on-chain records and smart contracts, Brainstems enables transparent and auditable data sharing agreements, ensuring that data contributions are accurately attributed and rewarded.