# SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning

arXiv: https://arxiv.org/abs/2412.17150 · HTML: https://mlsanity.com/paper/2412.17150/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients](https://mlsanity.com/paper/2201.11865/) (2022)
- [Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory](https://mlsanity.com/paper/2201.02664/) (2022)
- [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023)

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

- [Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learning](https://mlsanity.com/paper/2601.09076/) (2026)
- [AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning](https://mlsanity.com/paper/2607.17913/) (2026)
- [Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learning](https://mlsanity.com/paper/2603.09792/) (2026)
- [Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction](https://mlsanity.com/paper/2606.25003/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFed minimizes the computational burden of FL by balancing computation across clients and servers, while still preserving data privacy. This makes it an ideal learning framework across various domains, especially in healthcare, where data privacy is of utmost importance. However, SplitFed networks encounter numerous communication challenges, such as latency, bandwidth constraints, synchronization overhead, and a large amount of data that needs to be transferred during the learning process. In this paper, we propose SplitFedZip -- a novel method that employs learned compression to reduce data transfer in SplitFed learning. Through experiments on medical image segmentation, we show that learned compression can provide a significant data communication reduction in SplitFed learning, while maintaining the accuracy of the final trained model. The implementation is available at: urlhttps://github.com/ChamaniS/SplitFedZip.