# Optimizing Split Points for Error-Resilient SplitFed Learning

arXiv: https://arxiv.org/abs/2405.19453 · HTML: https://mlsanity.com/paper/2405.19453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019)
- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation](https://mlsanity.com/paper/2104.10195/) (2021)
- [Accelerating Split Federated Learning over Wireless Communication Networks](https://mlsanity.com/paper/2310.15584/) (2023)
- [SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments](https://mlsanity.com/paper/2205.10635/) (2022)
- [Loss Tolerant Federated Learning](https://mlsanity.com/paper/2105.03591/) (2021)
- [CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligence](https://mlsanity.com/paper/2106.05531/) (2021)
- [Federated Learning with Erroneous Communication Links](https://mlsanity.com/paper/2201.12991/) (2022)
- [Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations](https://mlsanity.com/paper/2304.14976/) (2023)

## Built on by (most cited first, 1 of 1)

- [Traversal Learning: A Lossless And Efficient Distributed Learning Framework](https://mlsanity.com/paper/2504.07471/) (2025)

## Abstract (arXiv)

Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machine learning. SplitFed aims to minimize the computational burden on individual clients in FL and parallelize SL while maintaining privacy. This study investigates the resilience of SplitFed to packet loss at model split points. It explores various parameter aggregation strategies of SplitFed by examining the impact of splitting the model at different points-either shallow split or deep split-on the final global model performance. The experiments, conducted on a human embryo image segmentation task, reveal a statistically significant advantage of a deeper split point.