Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline
  for Federated Image Classifications
Yusen Wu Jamie Deng Hao Chen Phuong Nguyen Yelena Yesha
Federated Learning (FL) has revolutionized how we train deep neural networks
by enabling decentralized collaboration while safeguarding sensitive data and
improving model performance. However, FL faces two crucial challenges: the
diverse nature of data held by individual clients and the vulnerability of the
FL system to security breaches. This paper introduces an innovative solution
named Estimated Mean Aggregation (EMA) that not only addresses these challenges
but also provides a fundamental reference point as a $\mathsf{baseline}$ for
advanced aggregation techniques in FL systems. EMA's significance lies in its
dual role: enhancing model security by effectively handling malicious outliers
through trimmed means and uncovering data heterogeneity to ensure that trained
models are adaptable across various client datasets. Through a wealth of
experiments, EMA consistently demonstrates high accuracy and area under the
curve (AUC) compared to alternative methods, establishing itself as a robust
baseline for evaluating the effectiveness and security of FL aggregation
methods. EMA's contributions thus offer a crucial step forward in advancing the
efficiency, security, and versatility of decentralized deep learning in the
context of FL.