MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916654368161792 |
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| author | Harshit |
| author_facet | Harshit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts Harshit Machine Learning Artificial Intelligence Computation and Language 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. |
| title | MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.12592 |