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MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts

Harshit

TL;DR

MoECollab tackles the centralization barrier in LLM development by introducing a collaborative Mixture of Experts framework that decomposes a monolithic model into domain-specific experts coordinated by a trainable gating network. The paper provides a complete implementation, formalizes expert routing with entropy regularization, and presents a robust tensor integration strategy for heterogeneous outputs. Empirically, MoECollab yields 3–7% accuracy gains over baselines and reduces computational requirements by about 34%, with notable domain gains (e.g., general F1 improving from 51% to 88% and news accuracy from 23% to 44%), alongside up to 14% higher expert utilization due to optimized routing. This work demonstrates that architecturally structured collaboration can democratize LLM development, improving performance, inclusivity, and resource efficiency across diverse domains.

Abstract

Large Language Model (LLM) development has become increasingly centralized, limiting participation to well-resourced organizations. This paper introduces MoECollab, a novel framework leveraging Mixture of Experts (MoE) architecture to enable distributed, collaborative LLM development. By decomposing monolithic models into specialized expert modules coordinated by a trainable gating network, our framework allows diverse contributors to participate regardless of computational resources. We provide a complete technical implementation with mathematical foundations for expert dynamics, gating mechanisms, and integration strategies. Experiments on multiple datasets demonstrate that our approach achieves accuracy improvements of 3-7% over baseline models while reducing computational requirements by 34%. Expert specialization yields significant domain-specific gains, with improvements from 51% to 88% F1 score in general classification and from 23% to 44% accuracy in news categorization. We formalize the routing entropy optimization problem and demonstrate how proper regularization techniques lead to 14% higher expert utilization rates. These results validate MoECollab as an effective approach for democratizing LLM development through architecturally-supported collaboration.

MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts

TL;DR

MoECollab tackles the centralization barrier in LLM development by introducing a collaborative Mixture of Experts framework that decomposes a monolithic model into domain-specific experts coordinated by a trainable gating network. The paper provides a complete implementation, formalizes expert routing with entropy regularization, and presents a robust tensor integration strategy for heterogeneous outputs. Empirically, MoECollab yields 3–7% accuracy gains over baselines and reduces computational requirements by about 34%, with notable domain gains (e.g., general F1 improving from 51% to 88% and news accuracy from 23% to 44%), alongside up to 14% higher expert utilization due to optimized routing. This work demonstrates that architecturally structured collaboration can democratize LLM development, improving performance, inclusivity, and resource efficiency across diverse domains.

Abstract

Large Language Model (LLM) development has become increasingly centralized, limiting participation to well-resourced organizations. This paper introduces MoECollab, a novel framework leveraging Mixture of Experts (MoE) architecture to enable distributed, collaborative LLM development. By decomposing monolithic models into specialized expert modules coordinated by a trainable gating network, our framework allows diverse contributors to participate regardless of computational resources. We provide a complete technical implementation with mathematical foundations for expert dynamics, gating mechanisms, and integration strategies. Experiments on multiple datasets demonstrate that our approach achieves accuracy improvements of 3-7% over baseline models while reducing computational requirements by 34%. Expert specialization yields significant domain-specific gains, with improvements from 51% to 88% F1 score in general classification and from 23% to 44% accuracy in news categorization. We formalize the routing entropy optimization problem and demonstrate how proper regularization techniques lead to 14% higher expert utilization rates. These results validate MoECollab as an effective approach for democratizing LLM development through architecturally-supported collaboration.

Paper Structure

This paper contains 20 sections, 8 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mixture of Experts in Language Models
  4. Collaborative AI Development
  5. Distributed and Federated Learning
  6. MoECollab Framework
  7. Architecture Overview
  8. Expert Module Design
  9. Gating Mechanism
  10. Tensor Integration with Heterogeneous Experts
  11. Experimental Evaluation
  12. Datasets and Experimental Setup
  13. Performance Comparison
  14. Expert Utilization and Performance Improvement
  15. Expert Specialization Analysis
  16. ...and 5 more sections

Figures (3)

  • Figure 1: The MoECollab architecture. Input text is processed by a shared encoder, and the gating network computes weights to route the input to specialized domain experts. The final output is a weighted combination of expert outputs.
  • Figure 2: Expert utilization patterns during training on general domain data. Expert 1 gradually specializes in this domain.
  • Figure 3: Performance improvement over training epochs for different domains.