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Mixture of Experts (MoE): A Big Data Perspective

Wensheng Gan, Zhenyao Ning, Zhenlian Qi, Philip S. Yu

TL;DR

This paper surveys Mixture of Experts as a scalable paradigm for big data, detailing its core principle of distributing tasks across specialized experts via a gating network to achieve divide-and-conquer learning. It reviews architectural variants, a formalized sparse routing mechanism, and the key technologies enabling MoE to handle high dimensionality, multisource data, and online dynamics, while also addressing interpretability and deployment concerns. Through extensive domain case studies in NLP, computer vision, recommendations, and cross disciplinary applications, the paper demonstrates MoE's potential to improve scalability, efficiency, and generalization in real-world big data settings. It further outlines challenges such as load imbalance and gating stability and sketches future directions including improved generalization, privacy preserving frameworks, automated systems, and deeper integration with other AI technologies to broaden MoE adoption.

Abstract

As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has shown excellent performance and broad application prospects. This paper provides an in-depth review and analysis of the latest progress in this field from multiple perspectives, including the basic principles, algorithmic models, key technical challenges, and application practices of MoE. First, we introduce the basic concept of MoE and its core idea and elaborate on its advantages over traditional single models. Then, we discuss the basic architecture of MoE and its main components, including the gating network, expert networks, and learning algorithms. Next, we review the applications of MoE in addressing key technical issues in big data. For each challenge, we provide specific MoE solutions and their innovations. Furthermore, we summarize the typical use cases of MoE in various application domains. This fully demonstrates the powerful capability of MoE in big data processing. We also analyze the advantages of MoE in big data environments. Finally, we explore the future development trends of MoE. We believe that MoE will become an important paradigm of artificial intelligence in the era of big data. In summary, this paper systematically elaborates on the principles, techniques, and applications of MoE in big data processing, providing theoretical and practical references to further promote the application of MoE in real scenarios.

Mixture of Experts (MoE): A Big Data Perspective

TL;DR

This paper surveys Mixture of Experts as a scalable paradigm for big data, detailing its core principle of distributing tasks across specialized experts via a gating network to achieve divide-and-conquer learning. It reviews architectural variants, a formalized sparse routing mechanism, and the key technologies enabling MoE to handle high dimensionality, multisource data, and online dynamics, while also addressing interpretability and deployment concerns. Through extensive domain case studies in NLP, computer vision, recommendations, and cross disciplinary applications, the paper demonstrates MoE's potential to improve scalability, efficiency, and generalization in real-world big data settings. It further outlines challenges such as load imbalance and gating stability and sketches future directions including improved generalization, privacy preserving frameworks, automated systems, and deeper integration with other AI technologies to broaden MoE adoption.

Abstract

As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has shown excellent performance and broad application prospects. This paper provides an in-depth review and analysis of the latest progress in this field from multiple perspectives, including the basic principles, algorithmic models, key technical challenges, and application practices of MoE. First, we introduce the basic concept of MoE and its core idea and elaborate on its advantages over traditional single models. Then, we discuss the basic architecture of MoE and its main components, including the gating network, expert networks, and learning algorithms. Next, we review the applications of MoE in addressing key technical issues in big data. For each challenge, we provide specific MoE solutions and their innovations. Furthermore, we summarize the typical use cases of MoE in various application domains. This fully demonstrates the powerful capability of MoE in big data processing. We also analyze the advantages of MoE in big data environments. Finally, we explore the future development trends of MoE. We believe that MoE will become an important paradigm of artificial intelligence in the era of big data. In summary, this paper systematically elaborates on the principles, techniques, and applications of MoE in big data processing, providing theoretical and practical references to further promote the application of MoE in real scenarios.

Paper Structure

This paper contains 46 sections, 2 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Mixture of Experts
  3. Basic Concepts and Development of MoE
  4. Basic Architecture and Mathematical Model
  5. Architecture of MoE
  6. An Example of the Computational Method
  7. Advantages
  8. Key Technologies of MoE for Big Data
  9. High-dimensional Sparse Data Modeling
  10. Heterogeneous Multisource Data Fusion
  11. Real-time Online Learning
  12. Enhancing Model Interpretability
  13. Other Key Technologies
  14. Optimized Design of Gating Networks
  15. Heterogeneous Design of Expert Networks
  16. ...and 31 more sections

Figures (5)

  • Figure 1: An overview of the chronological development of the MoE models.
  • Figure 2: Gating mechanism for MoE model schematic.
  • Figure 3: Expert network for the MoE model schematic.
  • Figure 4: The simple schematic of how the MoE model works.
  • Figure 5: Typical use cases of the MoE model for big data.