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Architectural Blueprint For Heterogeneity-Resilient Federated Learning

Satwat Bashir, Tasos Dagiuklas, Kasra Kassai, Muddesar Iqbal

TL;DR

This paper tackles the challenge of data and compute heterogeneity in Federated Learning for edge environments. It introduces a novel three-tier architecture consisting of a Client Layer, Edge Layer, and Fedge Layer, enabling multi-global models and dual-level aggregation to manage non-IID data efficiently while preserving privacy. The approach demonstrates improved model accuracy, reduced communication overhead, and scalability on MNIST under non-IID scenarios, compared to standard FedAvg. The work offers a practical blueprint for deploying heterogeneous FL at the edge, with implications for privacy-preserving distributed learning and broader adoption of FL technologies.

Abstract

This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.

Architectural Blueprint For Heterogeneity-Resilient Federated Learning

TL;DR

This paper tackles the challenge of data and compute heterogeneity in Federated Learning for edge environments. It introduces a novel three-tier architecture consisting of a Client Layer, Edge Layer, and Fedge Layer, enabling multi-global models and dual-level aggregation to manage non-IID data efficiently while preserving privacy. The approach demonstrates improved model accuracy, reduced communication overhead, and scalability on MNIST under non-IID scenarios, compared to standard FedAvg. The work offers a practical blueprint for deploying heterogeneous FL at the edge, with implications for privacy-preserving distributed learning and broader adoption of FL technologies.

Abstract

This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.
Paper Structure (17 sections, 6 figures)

This paper contains 17 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Background on Federated Learning
  3. The Multifaceted Nature of Heterogeneity
  4. Tackling Non-IID Data Challenges
  5. Critical Analysis of Aggregation Methods
  6. Towards Comprehensive Solutions
  7. Theoretical Considerations for Improved FL Frameworks
  8. Rationale for a Multi-Global Model Framework
  9. Innovation
  10. Empirical Findings on Federated Learning with Non-IID Data
  11. System Architecture and Experimental Setup
  12. Scenario Design
  13. Methodology and Model Implementation
  14. Parameters and Data Handling
  15. Findings, Behaviors, and Insights
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Proposed three-layered architecture, delineating the interactions between the client level, edge layer, and fedge layer
  • Figure 2: The sequence diagram illustrates the dynamic interactions within the proposed architecture, focusing on the multi-global model strategy within a hierarchical framework
  • Figure 3: Accuracy of Standard FL for non-IID Scenario 1
  • Figure 4: Accuracy of Three Layered FL for non-IID Scenario 1
  • Figure 5: Accuracy of Standard FL for non-IID Scenario 2
  • ...and 1 more figures