Communication Heterogeneity

Communication heterogeneity in federated learning (FL) refers to the uneven distribution of computational resources, communication bandwidth, and data quality across participating devices. Current research focuses on developing algorithms that efficiently manage these disparities, often employing techniques like dynamic resource allocation, low-rank model adaptations, and clustering strategies to optimize model accuracy while minimizing communication overhead. Addressing communication heterogeneity is crucial for the practical deployment of FL, enabling robust and efficient training of machine learning models in diverse and resource-constrained environments. This improves the scalability and applicability of FL across various domains.