Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data

• URL: http://arxiv.org/abs/2510.19535v1
• Date: Wed, 22 Oct 2025 12:41:04 GMT
• Title: Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data
• Authors: Markus Bujotzek, Evelyn Trautmann, Calum Hand, Ian Hales,
• Abstract summary: Federated learning (FL) offers a promising approach to integrate private data into privacy-preserving, collaborative model training across data silos.<n>We benchmark three approaches, Federated kMeans (Fed-kMeans), Federated Principal Component Analysis combined with Fed-kMeans (Fed-PCA+Fed-kMeans), and Federated Locality-Sensitive Hashing (Fed-LSH) against their centralized counterparts on eight diverse molecular datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: AI methods are increasingly shaping pharmaceutical drug discovery. However, their translation to industrial applications remains limited due to their reliance on public datasets, lacking scale and diversity of proprietary pharmaceutical data. Federated learning (FL) offers a promising approach to integrate private data into privacy-preserving, collaborative model training across data silos. This federated data access complicates important data-centric tasks such as estimating dataset diversity, performing informed data splits, and understanding the structure of the combined chemical space. To address this gap, we investigate how well federated clustering methods can disentangle and represent distributed molecular data. We benchmark three approaches, Federated kMeans (Fed-kMeans), Federated Principal Component Analysis combined with Fed-kMeans (Fed-PCA+Fed-kMeans), and Federated Locality-Sensitive Hashing (Fed-LSH), against their centralized counterparts on eight diverse molecular datasets. Our evaluation utilizes both, standard mathematical and a chemistry-informed evaluation metrics, SF-ICF, that we introduce in this work. The large-scale benchmarking combined with an in-depth explainability analysis shows the importance of incorporating domain knowledge through chemistry-informed metrics, and on-client explainability analyses for federated diversity analysis on molecular data.

Related papers

• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• Federated brain tumor segmentation: an extensive benchmark [2.515027627030043]
  We propose an extensive benchmark of federated learning algorithms from all three classes on this task. We show that some methods from each category can bring a slight performance improvement and potentially limit the final model(s) bias toward the predominant data distribution of the federation.
  arXiv  Detail & Related papers  (2024-10-07T09:32:19Z)
• Federated Impression for Learning with Distributed Heterogeneous Data [19.50235109938016]
  Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data. In FL, sub-optimal convergence is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression.
  arXiv  Detail & Related papers  (2024-09-11T15:37:52Z)
• Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt [1.0691609140312175]
  FedProt is the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data. It achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences. FedProt is available as a web tool with detailed documentation as a FeatureCloud App.
  arXiv  Detail & Related papers  (2024-07-21T17:09:20Z)
• MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis [18.940529282539842]
  We construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules. Our dataset offers significant physicochemical interpretability to guide model development and design. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning.
  arXiv  Detail & Related papers  (2024-06-13T02:50:23Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• Neural FIM for learning Fisher Information Metrics from point cloud data [71.07939200676199]
  We propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data. We demonstrate its utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells)
  arXiv  Detail & Related papers  (2023-06-01T17:36:13Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Federated Learning of Molecular Properties in a Heterogeneous Setting [79.00211946597845]
  We introduce federated heterogeneous molecular learning to address these challenges. Federated learning allows end-users to build a global model collaboratively while preserving the training data distributed over isolated clients. FedChem should enable a new type of collaboration for improving AI in chemistry that mitigates concerns about valuable chemical data.
  arXiv  Detail & Related papers  (2021-09-15T12:49:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.