Network Aggregation

Network aggregation focuses on optimizing the communication efficiency of distributed computing, particularly in deep learning, by performing computations within the network infrastructure rather than solely on individual nodes. Current research emphasizes techniques like in-network aggregation (INA) integrated with various architectures such as Ring-AllReduce and Parameter Servers, often incorporating novel sparsification and compression methods to reduce communication overhead. These advancements aim to accelerate training of large models and improve scalability in applications like federated learning and distributed deep learning, ultimately impacting the speed and feasibility of training complex AI models.