# SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression

arXiv: https://arxiv.org/abs/2602.10564 · HTML: https://mlsanity.com/paper/2602.10564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2026.

## Built on by (most cited first, 4 of 4)

- [RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces](https://mlsanity.com/paper/2602.20618/) (2026)
- [Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction](https://mlsanity.com/paper/2606.25003/) (2026)
- [SplitLite: Low-Rank Residual Compression for Split Learning](https://mlsanity.com/paper/2608.23018/) (2026)
- [CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization](https://mlsanity.com/paper/2609.15664/) (2026)

## Abstract (arXiv)

Federated fine-tuning of on-device large language models (LLMs) mitigates privacy concerns by preventing raw data sharing. However, the intensive computational and memory demands pose significant challenges for resource-constrained edge devices. To overcome these limitations, split federated learning (SFL) emerges as a promising solution that partitions the model into lightweight client-side and compute-intensive server-side sub-models, thus offloading the primary training workload to a powerful server. Nevertheless, high-dimensional activation exchanges in SFL lead to excessive communication overhead. To overcome this, we propose SplitCom, a communication-efficient SFL framework for LLMs that exploits temporal redundancy in activations across consecutive training epochs. Inspired by video compression, the core innovation of our framework lies in selective activation uploading only when a noticeable deviation from previous epochs occurs. To balance communication efficiency and learning performance, we introduce two adaptive threshold control schemes based on 1) bang-bang control or 2) deep deterministic policy gradient (DDPG)-based reinforcement learning. Moreover, we implement dimensionality reduction techniques to alleviate client-side memory requirements. Furthermore, we extend SplitCom to the U-shape architecture, ensuring the server never accesses clients' labels. Extensive simulations and laboratory experiments demonstrate that SplitCom reduces uplink communication costs by up to 98.6,% in its standard configuration and total communication costs by up to 95.8,% in its U-shape variant without noticeably compromising model performance.