Drift Plus Penalty

Drift-plus-penalty methods address challenges arising from data heterogeneity and model divergence in optimization problems, particularly within evolutionary algorithms and federated learning. Current research focuses on refining these methods, including developing variants with improved regularization techniques (e.g., doubly regularized drift correction) to mitigate issues like genetic drift and client drift, and adapting them to handle Markovian data and bandit feedback scenarios. These advancements aim to enhance the efficiency and robustness of optimization algorithms across diverse applications, improving performance in settings where data is non-independent and identically distributed (non-IID) or where communication costs are a significant constraint.