# Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data

arXiv: https://arxiv.org/abs/2402.10135 · HTML: https://mlsanity.com/paper/2402.10135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)

## Built on by (most cited first, 3 of 3)

- [dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis](https://mlsanity.com/paper/2503.10412/) (2025)
- [A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications](https://mlsanity.com/paper/2412.06494/) (2024)
- [Loss-Guided Model Sharing and Local Learning Correction in Decentralized Federated Learning for Crop Disease Classification](https://mlsanity.com/paper/2505.23063/) (2025)

## Abstract (arXiv)

The increasing requirements for data protection and privacy has attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was centralised and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most straightforward averaging strategy. This research is focused on testing different federated strategies in a peer-to-peer environment. The authors propose various aggregation strategies for federated learning, including weighted averaging aggregation, using different factors and strategies based on participant contribution. The strategies are tested with varying data sizes to identify the most robust ones. This research tests the strategies with several biomedical datasets and the results of the experiments show that the accuracy-based weighted average outperforms the classical federated averaging method.