#split-learning News & Analysis

3 articles tagged with #split-learning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Mar 177/10

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HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

Researchers propose HO-SFL (Hybrid-Order Split Federated Learning), a new framework that enables memory-efficient fine-tuning of large AI models on edge devices by eliminating backpropagation on client devices while maintaining convergence speed comparable to traditional methods. The approach significantly reduces communication costs and memory requirements for distributed AI training.

AINeutralarXiv – CS AI · Jun 106/10

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QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

QSplitFL introduces a Deep Q-Network framework that optimizes split point selection in federated learning by considering device heterogeneity, using lightweight hardware metrics instead of model weights. The approach demonstrates improved convergence and accuracy across multiple datasets and neural network architectures while adapting to varying client capabilities.

AIBullisharXiv – CS AI · Apr 156/10

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Fast AI Model Partition for Split Learning over Edge Networks

Researchers propose an optimal model partitioning algorithm for split learning that reduces training delays by up to 38.95% by representing AI models as directed acyclic graphs and solving the problem via maximum-flow methods. The approach includes a low-complexity block-wise algorithm that achieves 13x faster computation on edge computing hardware, advancing the feasibility of distributed AI inference on mobile and edge devices.

🏢 Nvidia