Decentralized Mixture of Experts (dMoE)

A decentralized mixture of experts is a collaborative system where specialized nodes or participants collectively solve problems, with a decentralized network autonomously selecting the best-suited participant to handle each task.

What Is A Decentralized Mixture of Experts?

In traditional models, a mixture of experts (MoE) defines a machine learning model comprising multiple “experts,” where each expert specializes in solving individual tasks. In other words, it splits complex models or tasks among smaller, more specialized networks known as “expert networks.” Each expert is trained on a specific aspect of the bigger task or data subset.

A decentralized mixture of experts (dMoE) models adapts this concept to a decentralized network, such as a blockchain. This means that rather than a central entity controlling the experts, the decision-making and control are spread across multiple smaller systems – called gate networks – hosted on peer devices. Simply put, the network autonomously selects the most suitable expert (a node or smart contract) based on what the task needs.

What Are the Key Components of a dMoE?

Some of the main components of a dMoE model include:

• Multiple gating mechanisms – Think of a gate network as the manager that determines the expert best suited for a specific task. Rather than a single central gate, decentralized MoE utilizes multiple, smaller gates to determine which expert to use. 
• Experts – An expert is a smaller model trained to be good at a specific aspect of a larger problem, such as translating texts or images. A gating network independently selects the most relevant expert based on the specific requirements of that task.
• Localized decision-making – Gate networks in a dMoE are somewhat parallel decision-makers, meaning that each gate is responsible for managing different parts of the problem. This also means they can independently decide which expert to activate for a task without instructions from a central authority.
• Distributed interactions – In a dMoE network, the gate networks and experts are technically distributed. For more effective communication between them, the network partitions and sends data to the appropriate gate, which then transmits the relevant data to the chosen experts. This facilitates parallel processing, allowing the overall network to manage multiple tasks simultaneously.