A decentralized Mixture of Experts (MoE) system is a model that enhances performance by using multiple specialized experts and gates for parallel, efficient data processing.
With traditional models, everything is handled by one general system that has to deal with everything at once. MoE splits tasks into specialized experts, making it more efficient. And dMoE distributes decision-making across smaller systems, which helps when you’re working with big data or a lot of machines.
Traditionally, machine learning models worked by using one big, general-purpose model to handle everything. Imagine a single expert trying to handle every task: It might be okay at some things but not great at others. For example, if you had a model trying to recognize both faces and text in the same system, the model would have to learn both tasks together, which could make it slower and less efficient.