Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G

2024-05-06

arXiv.org

AI Summary

To address the challenges of high communication overhead, strict synchronization requirements, substantial computational demand, and non-independent and identically distributed (Non-IID) data in distributed learning for 6G networks, this paper proposes an inter-layer serpentine sequential update mechanism. The method dynamically allocates model layer training tasks across nodes based on heterogeneous computational capabilities and local data distributions, integrating hierarchical model partitioning, serialized node scheduling, and lightweight gradient transmission. Crucially, it eliminates global synchronization and avoids maintaining a complete model replica at any single node. As a result, the approach significantly reduces both communication and memory overhead. Empirical evaluation demonstrates consistently high accuracy and rapid convergence on both classification and fine-tuning tasks under both IID and Non-IID data settings. This work establishes a novel, efficient, and scalable paradigm for distributed learning tailored to the stringent requirements of intelligent 6G networks.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the evolution towards 6G, integrating Artificial Intelligence (AI) with advanced network infrastructure emerges as a pivotal strategy for enhancing network intelligence and resource utilization. Existing distributed learning frameworks like Federated Learning and Split Learning often struggle with significant challenges in dynamic network environments including high synchronization demands, costly communication overhead, severe computing resource consumption, and data heterogeneity across network nodes. These obstacles hinder the applications of ubiquitous computing capabilities of 6G networks, especially in light of the trend of escalating model parameters and training data volumes. To address these challenges effectively, this paper introduces ``Snake Learning", a cost-effective distributed learning framework. Specifically, Snake Learning respects the heterogeneity of inter-node computing capability and local data distribution in 6G networks, and sequentially trains the designated part of model layers on individual nodes. This layer-by-layer serpentine update mechanism contributes to significantly reducing the requirements for storage, memory and communication during the model training phase, and demonstrates superior adaptability and efficiency for both classification and fine-tuning tasks across homogeneous and heterogeneous data distributions.

Problem

• Distributed Learning
• 6G Networks
• Model Parameters

Innovation

• Snake Learning
• Distributed Learning
• 6G Networks