FedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. Conditions

TL;DR

FedTopo introduces a topology-aware federated learning framework that aligns client representations by leveraging topological features, significantly improving convergence and accuracy in non-I.I.D. visual data scenarios.

Contribution

The paper proposes a novel topology-informed approach with TGBS, TE, and TAL to enhance federated learning under heterogeneous data conditions.

Findings

Accelerates convergence in federated learning.

Improves accuracy on non-I.I.D. datasets.

Effective topological feature alignment across clients.

Abstract

Current federated-learning models deteriorate under heterogeneous (non-I.I.D.) client data, as their feature representations diverge and pixel- or patch-level objectives fail to capture the global topology which is essential for high-dimensional visual tasks. We propose FedTopo, a framework that integrates Topological-Guided Block Screening (TGBS) and Topological Embedding (TE) to leverage topological information, yielding coherently aligned cross-client representations by Topological Alignment Loss (TAL). First, Topology-Guided Block Screening (TGBS) automatically selects the most topology-informative block, i.e., the one with maximal topological separability, whose persistence-based signatures best distinguish within- versus between-class pairs, ensuring that subsequent analysis focuses on topology-rich features. Next, this block yields a compact Topological Embedding, which quantifies the topological information for each client. Finally, a Topological Alignment Loss (TAL) guides clients to maintain topological consistency with the global model during optimization, reducing representation drift across rounds. Experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 under four non-I.I.D. partitions show that FedTopo accelerates convergence and improves accuracy over strong baselines.