Self-Organizing Democratized Learning: Toward Large-Scale Distributed Learning Systems.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Distributed Systems

Background:

• Cross-device AI applications necessitate a shift from centralized to large-scale distributed learning systems.
• Existing mechanisms like federated learning (FL) have limitations in generalizing across diverse, personalized datasets.

Purpose of the Study:

• To introduce democratized learning (Dem-AI) as a philosophy and framework for large-scale, distributed, and democratized machine learning systems.
• To propose a novel distributed learning approach inspired by Dem-AI principles, enhancing generalization beyond FL.

Main Methods:

• A self-organizing hierarchical structure using agglomerative clustering and hierarchical generalization.
• Formulation of hierarchical generalized learning problems solved via distributed personalized learning and hierarchical updates.
• Introduction of the DemLearn distributed learning algorithm.

Main Results:

• The DemLearn algorithm demonstrated superior generalization performance compared to conventional FL algorithms on benchmark datasets (MNIST, Fashion-MNIST, FE-MNIST, CIFAR-10).
• Experimental results validate the effectiveness of the proposed hierarchical structuring and learning mechanisms.

Conclusions:

• The proposed Dem-AI approach offers a promising direction for building robust distributed AI systems capable of handling personalized data.
• Further analysis provides insights into managing both generalization and specialization in Dem-AI systems.