Latency-Aware Client Assignment for Parallel Split Learning With Global Sampling

arXiv:2609.32132 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Comments: 10 pages, 5 figures. Submitted to IEEE Transactions on Emerging Topics in Computational Intelligence

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 32× in this paper · explained in 1 episode
• Federated Learning — 10× in this paper · explained in 14 episodes
• Regret — 7× in this paper · explained in 3 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Differentially Private — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Split learning for health: Distributed deep learning without sharing raw patient data
• Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
• GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

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