Nathan Gaw

Convergence, Bias, and Fairness in Federated Learning: A Non-Stationary Multi-Armed Bandit Approach for Heterogeneous Client Selection

In practical federated learning (FL) environments, clients often possess non-IID data, which can degrade model performance and extend convergence times. Effective client selection strategies have emerged as a promising approach to mitigate the challenges posed by statistical heterogeneity across clients. This paper pro...