SplitLite: Low-Rank Residual Compression for Split Learning

arXiv:2608.23018 · cs.LG, cs.AI · Submitted 2026-08-24 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-24

Updated: 2026-09-05

Comments: The manuscript requires further refinement in writing presentation and theoretical derivations to improve its clarity and rigor. We therefore wish to withdraw the current submission for further revision

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

Terminology

• Fine-Tuning — 38× in this paper · explained in 5 episodes
• Quantization — 23× in this paper · explained in 12 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• LoRA Adapters — 11× in this paper · explained in 3 episodes
• Federated Learning — 10× in this paper · explained in 15 episodes
• Communication Overhead — 9× in this paper · explained in 2 episodes
• Aggregation — 8× in this paper · explained in 1 episode
• Edge Devices — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 6× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Singular Value Decomposition (SVD) — 3× in this paper · explained in 2 episodes
• Model Drift — 2× in this paper · explained in 4 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Edge AI — 1× in this paper · explained in 2 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
• SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
• SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression
• Massive Activations in Large Language Models
• Split learning for health: Distributed deep learning without sharing raw patient data
• AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
• FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
• Reducing Communication for Split Learning by Randomized Top-k Sparsification
• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
• Qwen2.5 Technical Report
• Gemma 3 Technical Report
• The Llama 3 Herd of Models

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