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Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning

Zahir Alsulaimawi

TL;DR

Empirical evaluation across four healthcare-related datasets demonstrates the Meta-FL framework's adaptability, efficiency, scalability, and robustness, outperforming conventional FL approaches.

Abstract

Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and model diversity. The Meta-Federated Learning (Meta-FL) framework has been introduced to tackle these challenges. Meta-FL employs an optimization-based Meta-Aggregator to navigate the complexities of heterogeneous model updates. The Meta-Aggregator enhances the global model's performance by leveraging meta-features, ensuring a tailored aggregation that accounts for each local model's accuracy. Empirical evaluation across four healthcare-related datasets demonstrates the Meta-FL framework's adaptability, efficiency, scalability, and robustness, outperforming conventional FL approaches. Furthermore, Meta-FL's remarkable efficiency and scalability are evident in its achievement of superior accuracy with fewer communication rounds and its capacity to manage expanding federated networks without compromising performance.

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning

TL;DR

Empirical evaluation across four healthcare-related datasets demonstrates the Meta-FL framework's adaptability, efficiency, scalability, and robustness, outperforming conventional FL approaches.

Abstract

Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and model diversity. The Meta-Federated Learning (Meta-FL) framework has been introduced to tackle these challenges. Meta-FL employs an optimization-based Meta-Aggregator to navigate the complexities of heterogeneous model updates. The Meta-Aggregator enhances the global model's performance by leveraging meta-features, ensuring a tailored aggregation that accounts for each local model's accuracy. Empirical evaluation across four healthcare-related datasets demonstrates the Meta-FL framework's adaptability, efficiency, scalability, and robustness, outperforming conventional FL approaches. Furthermore, Meta-FL's remarkable efficiency and scalability are evident in its achievement of superior accuracy with fewer communication rounds and its capacity to manage expanding federated networks without compromising performance.
Paper Structure (35 sections, 3 theorems, 14 equations, 6 figures, 1 algorithm)

This paper contains 35 sections, 3 theorems, 14 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Addressing Data Heterogeneity
  4. Enhancing Privacy Preservation
  5. Optimization and Aggregation Strategies
  6. Transitioning Towards Meta-Federated Learning
  7. Preliminaries
  8. Federated Learning
  9. Optimization in FL
  10. Meta-Learning
  11. Optimization-Based Meta-Aggregation
  12. Proposed Approach
  13. System Model and Notations
  14. Meta-Aggregator Formulation
  15. Optimization Strategy
  16. ...and 20 more sections

Key Result

Lemma 1

Given a sequence of meta-features $\{F_k^t\}_{t=1}^{\infty}$ and corresponding performance metrics $\{P_k^t\}_{t=1}^{\infty}$ for each client $k$, the weights $w_k(\phi^t)$ generated by the Meta-Aggregator converge to a fixed point as $t \to \infty$, assuming $F_k^t$ and $P_k^t$ satisfy certain regu

Figures (6)

  • Figure 1: Adaptability to New Tasks
  • Figure 2: Meta-feature Relevance Analysis
  • Figure 3: Model Generalization
  • Figure 4: Efficiency in Learning
  • Figure 5: Scalability: Average Accuracy vs. Number of Clients
  • ...and 1 more figures

Theorems & Definitions (6)

  • Lemma 1: Weight Convergence
  • proof
  • Theorem 2: Convergence to Optimal Aggregation
  • proof
  • Theorem 3: Generalization Bound
  • proof