Sign Based

Sign-based methods in machine learning focus on optimizing models using only the sign (positive or negative) of gradients, rather than their full magnitude, to reduce computational cost and communication overhead, particularly in distributed settings like federated learning. Current research emphasizes improving the convergence speed and robustness of sign-based algorithms, often employing variance reduction techniques and exploring their application within transformer networks and other architectures. These methods offer significant potential for enhancing the efficiency and scalability of training large models, especially in resource-constrained environments, while also addressing challenges posed by data heterogeneity and noisy feedback.