mlsanityData to 14 Oct 2026

Optimizing Split Points for Error-Resilient SplitFed Learning

arXiv 2405.19453 ↗First posted 29 May 2024cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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.

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13 of 23 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  3. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  4. 2018Distributed learning of deep neural network over multiple agentscited by 279
  5. 2019BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Servicescited by 68
  6. 2021Collaborative Intelligence: Challenges and Opportunitiescited by 19
  7. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
  8. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  9. 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environmentscited by 11
  10. 2021Loss Tolerant Federated Learningcited by 8
  11. 2021CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligencecited by 5
  12. 2022Federated Learning with Erroneous Communication Linkscited by 5
1 more
  1. 2023Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotationscited by 4

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