mlsanityData to 7 Oct 2026

Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments

arXiv 2310.18479 ↗First posted 27 Oct 2023cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 27 Oct 2023.

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17 of 30 references.

References · 17

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  2. 2019Towards Federated Learning at Scale: System Designcited by 826
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  5. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  6. 2018Distributed learning of deep neural network over multiple agentscited by 277
  7. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 104
  8. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  9. 2019Split Learning for collaborative deep learning in healthcarecited by 72
  10. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  11. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  12. 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
5 more
  1. 2018No Peek: A Survey of private distributed deep learningcited by 30
  2. 2020Distributed Training of Deep Learning Models: A Taxonomic Perspectivecited by 25
  3. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  4. 2019One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Predictioncited by 6
  5. 2020Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learningcited by 4

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