mlsanityData to 14 Oct 2026

A Hybrid Federated Kernel Regularized Least Squares Algorithm

arXiv 2407.17228 ↗First posted 24 Jul 2024stat.ML cs.LGcited by 0

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Abstract (arXiv)

Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings. Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics). Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples. This scenario leads to a hybrid setting where data is scattered both in terms of samples and features. In this hybrid setting, we present an efficient reformulation of the Kernel Regularized Least Squares algorithm, introduce two variants and validate them using well-established datasets. Lastly, we discuss security measures to defend against possible attacks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 57 references that are in the map. First posted on arXiv 24 Jul 2024.

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15 of 57 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Federated Learning with Matched Averagingcited by 564
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  5. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  6. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  7. 2015Less is More: Nystr"om Computational Regularizationcited by 91
  8. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  9. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
  10. 2019Practical Federated Gradient Boosting Decision Treescited by 54
  11. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  12. 2021An Efficient Federated Distillation Learning System for Multi-task Time Series Classificationcited by 14
3 more
  1. 2020PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networkscited by 10
  2. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  3. 2022A Primal-Dual Algorithm for Hybrid Federated Learningcited by 4

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