# Blind Federated Learning without initial model

arXiv: https://arxiv.org/abs/2404.16180 · HTML: https://mlsanity.com/paper/2404.16180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2008.06197/) (2020)
- [An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization](https://mlsanity.com/paper/2105.05717/) (2021)
- [Classification and Feature Transformation with Fuzzy Cognitive Maps](https://mlsanity.com/paper/2103.05124/) (2021)
- [A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets](https://mlsanity.com/paper/2402.10145/) (2024)

## Built on by (most cited first, 2 of 2)

- [The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications](https://mlsanity.com/paper/2510.17901/) (2025)
- [Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps](https://mlsanity.com/paper/2604.05136/) (2026)

## Abstract (arXiv)

Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two innovative methodologies for Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in a privacy-preserving way. In addition, one relevant contribution this research includes is the lack of an initial model in the federated learning process, making it effectively blind. This proposal is tested with several open datasets, improving both accuracy and precision.