FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling

TL;DR

FL-Sailer is a novel federated learning framework tailored for single-cell epigenetic data, addressing high dimensionality, sparsity, and heterogeneity while preserving privacy and improving analysis efficiency.

Contribution

It introduces adaptive leverage score sampling and an invariant VAE architecture, enabling scalable, privacy-preserving multi-institutional epigenomic analysis with theoretical guarantees.

Findings

Reduces feature dimensionality by 80% using adaptive sampling.

Outperforms centralized methods on synthetic and real datasets.

Enables multi-institutional collaboration in epigenomic research.

Abstract

Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving alternative, but faces three fundamental barriers in scATAC-seq analysis: ultra-high dimensionality, extreme sparsity, and severe cross-institutional heterogeneity. We propose FL-Sailer, the first FL framework designed for scATAC-seq data. FL-Sailer integrates two key innovations: (i) adaptive leverage score sampling, which selects biologically interpretable features while reducing dimensionality by 80%, and (ii) an invariant VAE architecture, which disentangles biological signals from technical confounders via mutual information minimization. We provide a convergence guarantee, showing that FL-Sailer converges to an approximate solution of the original high-dimensional problem with bounded error. Extensive experiments on synthetic and real epigenomic datasets demonstrate that FL-Sailer not only enables previously infeasible multi-institutional collaborations but also surpasses centralized methods by leveraging adaptive sampling as an implicit regularizer to suppress technical noise. Our work establishes that federated learning, when tailored to domain-specific challenges, can become a superior paradigm for collaborative epigenomic research.