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Federated Learning Can Find Friends That Are Advantageous

Nazarii Tupitsa, Samuel Horváth, Martin Takáč, Eduard Gorbunov

TL;DR

MeritFed introduces adaptive, merit-based aggregation in Federated Learning to leverage beneficial cross-client collaborations while mitigating detrimental ones. By solving a local auxiliary problem to optimize aggregation weights on the simplex, the server can weight gradients from all clients, achieving convergence guarantees that match or exceed using only same-distribution clients and exhibiting improved performance in heterogeneous settings. The method is accompanied by two practical approaches for the auxiliary problem—one using fresh data with zeroth-order Mirror Descent and another leveraging an extra validation dataset—along with theoretical guarantees under standard smoothness and variance assumptions and empirical validation on mean estimation, image, and text tasks. The work highlights the practical impact of judicious client selection in FL and lays groundwork for scalable, robust, and more efficient collaborative learning across diverse data distributions.

Abstract

In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. While collaboration among clients can significantly enhance the learning process, not all collaborations are beneficial; some may even be detrimental. In this study, we introduce a novel algorithm that assigns adaptive aggregation weights to clients participating in FL training, identifying those with data distributions most conducive to a specific learning objective. We demonstrate that our aggregation method converges no worse than the method that aggregates only the updates received from clients with the same data distribution. Furthermore, empirical evaluations consistently reveal that collaborations guided by our algorithm outperform traditional FL approaches. This underscores the critical role of judicious client selection and lays the foundation for more streamlined and effective FL implementations in the coming years.

Federated Learning Can Find Friends That Are Advantageous

TL;DR

MeritFed introduces adaptive, merit-based aggregation in Federated Learning to leverage beneficial cross-client collaborations while mitigating detrimental ones. By solving a local auxiliary problem to optimize aggregation weights on the simplex, the server can weight gradients from all clients, achieving convergence guarantees that match or exceed using only same-distribution clients and exhibiting improved performance in heterogeneous settings. The method is accompanied by two practical approaches for the auxiliary problem—one using fresh data with zeroth-order Mirror Descent and another leveraging an extra validation dataset—along with theoretical guarantees under standard smoothness and variance assumptions and empirical validation on mean estimation, image, and text tasks. The work highlights the practical impact of judicious client selection in FL and lays groundwork for scalable, robust, and more efficient collaborative learning across diverse data distributions.

Abstract

In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. While collaboration among clients can significantly enhance the learning process, not all collaborations are beneficial; some may even be detrimental. In this study, we introduce a novel algorithm that assigns adaptive aggregation weights to clients participating in FL training, identifying those with data distributions most conducive to a specific learning objective. We demonstrate that our aggregation method converges no worse than the method that aggregates only the updates received from clients with the same data distribution. Furthermore, empirical evaluations consistently reveal that collaborations guided by our algorithm outperform traditional FL approaches. This underscores the critical role of judicious client selection and lays the foundation for more streamlined and effective FL implementations in the coming years.
Paper Structure (16 sections, 2 theorems, 30 equations, 27 figures, 1 algorithm)

This paper contains 16 sections, 2 theorems, 30 equations, 27 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Setup
  3. Contribution
  4. Related work
  5. MeritFed: Merit-Based Federated Learning For Diverse Datasets
  6. The Proposed Method
  7. Auxiliary Problem in Line \ref{['lst:line:aux_problem']}
  8. Convergence Analysis
  9. Experiments
  10. Conclusion
  11. Weights Update for TAWT and FedAdp
  12. Proof of Theorem \ref{['thm:main_result']}
  13. Additional Experiments
  14. Results without Additional Validation Dataset
  15. Robustness against Byzantine Attacks
  16. ...and 1 more sections

Key Result

Theorem 3.4

Let Assumptions as:bounded-var and as:lipschitzness hold. Then after $T$ iterations, MeritFed with $\gamma \leq \frac{1}{2L}$ outputs $x^{i}$, $i=0,\cdots, T-1$ such that where $\delta$ is the accuracy of solving the problem in Line lst:line:aux_problem and $G = |{\cal G}|$. Moreover if Assumption as:pl additionally holds, then after $T$ iterations of MeritFed with $\gamma \leq \frac{1}{2L}$ outp

Figures (27)

  • Figure 1: Mean Estimation: $\mu = 0.001$, MD learning rate = 3.5.
  • Figure 2: Mean Estimation: $\mu = 0.01$, MD learning rate = 4.5.
  • Figure 3: Mean Estimation: $\mu = 0.1$, MD learning rate = 12.5.
  • Figure 4: CIFAR10 (extra val.): $\alpha = 0.5$
  • Figure 5: CIFAR10 (extra val.): $\alpha = 0.7$
  • ...and 22 more figures

Theorems & Definitions (3)

  • Theorem 3.4
  • Theorem B.1
  • proof