mlsanityData to 14 Oct 2026

Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation

arXiv 2601.18948 ↗First posted 26 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

Split-Federated (SplitFed) learning is an extension of federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of the overall model is deployed on the clients hardware. In SplitFed learning, feature values, gradient updates, and model updates are transferred across communication channels. In this paper, we study the effects of noise in the communication channels on the learning process and the quality of the final model. We propose a smart averaging strategy for SplitFed learning with the goal of improving resilience against channel noise. Experiments on a segmentation model for embryo images shows that the proposed smart averaging strategy is able to tolerate two orders of magnitude stronger noise in the communication channels compared to conventional averaging, while still maintaining the accuracy of the final model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 26 Jan 2026.

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10 of 19 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Federated Machine Learning: Concept and Applicationscited by 412
  4. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  5. 2018Distributed learning of deep neural network over multiple agentscited by 279
  6. 2019Split Learning for collaborative deep learning in healthcarecited by 73
  7. 2020Robust Federated Learning with Noisy Labelscited by 40
  8. 2021SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingcited by 19
  9. 2020Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcarecited by 14
  10. 2021Auto-weighted Robust Federated Learning with Corrupted Data Sourcescited by 12

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