Skip to content

Latest commit

 

History

History
63 lines (45 loc) · 2.63 KB

article.md

File metadata and controls

63 lines (45 loc) · 2.63 KB
Raw

Federated Learning Using Persistence Homology for Clustering and Robustness Evaluation

Abstract

Federated learning is an emerging paradigm that enables decentralized machine learning across multiple devices while preserving data privacy. However, the challenges of data heterogeneity and robustness in such distributed systems remain largely unaddressed. This paper introduces a novel federated learning algorithm that leverages Persistence Homology (PH) for clustering and robustness. PH, a topological data analysis tool, captures the multi-scale topological features inherent in data, providing a robust and natural way to understand its structure.

1. Introduction

  • 1.1 Background
    • Federated Learning
    • Persistence Homology
    • CKA
    • Clustering Algorithms
    • Pbow
    • Robustness in Machine Learning
  • 1.2 Problem Statement
  • 1.3 Objectives
  • 1.4 Contributions

Why Use PH for Clustering?

  1. Robustness to Noise: Traditional clustering methods often suffer from sensitivity to noise and outliers. PH inherently provides noise filtration, making the clustering more robust.

  2. Multi-Scale Analysis: PH allows for capturing features at various scales, which is crucial for handling data heterogeneity in federated settings.

  3. Invariant Features: Topological features are invariant under coordinate transformations, making PH a versatile tool for clustering in non-linear, high-dimensional spaces.

  4. Complexity Reduction: PH can simplify the complexity of the data space by focusing on essential topological features, thus making the federated learning process more efficient.

By integrating PH into the federated learning framework, we achieve enhanced clustering performance and robustness against various types of adversarial attacks and system failures. Experimental results on multiple datasets demonstrate the efficacy of our approach, setting a new benchmark for robust federated learning algorithms.

2. Related Work

  • 2.1 Federated Learning
  • 2.2 Persistence Homology in Machine Learning
  • 2.3 Clustering Algorithms
  • 2.4 Robustness in Distributed Systems

3. Preliminaries

  • 3.1 Mathematical Notations
  • 3.2 Basic Concepts
    • PH
    • Federated Learning Architecture

4. Methodology

4.1 Federated Learning Framework

  • 4.1.1 Data Distribution
  • 4.1.2 Aggregation Methods

4.3 Clustering Algorithm With PH

  • 4.3.1 Algorithm Description
  • 4.3.2 Complexity Analysis

5. Experiments and Results

  • 6.1 Experimental Setup
  • 6.2 Datasets
  • 6.3 Evaluation Metrics
  • 6.4 Results and Discussion

6. Conclusion

  • 7.1 Summary
  • 7.2 Future Work