Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
This addresses the problem of inefficient distributed learning in dynamic 6G network environments for network operators and AI developers, though it appears incremental as it builds on existing frameworks like Federated and Split Learning.
The paper tackles the challenges of high synchronization demands, communication overhead, and computing resource consumption in distributed learning for 6G networks by introducing Snake Learning, a framework that sequentially trains model layers on individual nodes, reducing storage, memory, and communication requirements while showing adaptability and efficiency in tasks.
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.