Communication Compression

Communication compression aims to reduce the bandwidth demands of distributed machine learning, a critical bottleneck in large-scale applications like federated learning. Current research focuses on developing algorithms that incorporate compression techniques (e.g., quantization, sparsification) while maintaining model accuracy, often employing strategies like error feedback and adaptive compression levels within decentralized and federated learning frameworks. These advancements are significant because they enable efficient training of complex models on resource-constrained devices and across geographically dispersed datasets, impacting both the scalability of machine learning and its applicability in bandwidth-limited environments.