mlsanityData to 7 Oct 2026

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

arXiv 2107.04129 ↗First posted 8 Jul 2021cs.LG cs.CR cs.DCcited by 4

Not on the map. Why?

Abstract (arXiv)

In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms. As the first batch of novel FL algorithm examples, we release vertical federated kernel binary classification model and vertical federated random forest model. They have been tested to be more efficient than existing vertical federated learning models in our practice. Besides the novel FL algorithm examples, we also release a machine communication module. The uniform data transfer interface supports transferring widely used data formats between machines. We will maintain this platform by adding more functional modules and algorithm examples. The code is available at https://github.com/fedlearnAI/fedlearn-algo.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 8 Jul 2021.

Builds on

10 of 17 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020The Future of Digital Health with Federated Learningcited by 625
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  4. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  5. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  7. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  8. 2018Private Model Compression via Knowledge Distillationcited by 37
  9. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
  10. 2020Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learningcited by 19

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2.

4 papers.

  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2021Dual Projection Generative Adversarial Networks for Conditional Image Generationcited by 8
  3. 2022An Efficient and Robust System for Vertically Federated Random Forestcited by 6
  4. 2022Deep Federated Anomaly Detection for Multivariate Time Series Datacited by 3