# A Hybrid Federated Kernel Regularized Least Squares Algorithm

arXiv: https://arxiv.org/abs/2407.17228 · HTML: https://mlsanity.com/paper/2407.17228/

## 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.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [When the Curious Abandon Honesty: Federated Learning Is Not Private](https://mlsanity.com/paper/2112.02918/) (2021)
- [Less is More: Nystr"om Computational Regularization](https://mlsanity.com/paper/1507.04717/) (2015)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks](https://mlsanity.com/paper/2004.02264/) (2020)
- [Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning](https://mlsanity.com/paper/2404.10110/) (2024)
- [A Primal-Dual Algorithm for Hybrid Federated Learning](https://mlsanity.com/paper/2210.08106/) (2022)

## 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.