Gradient Coding

Gradient coding is a technique used to improve the efficiency and robustness of distributed machine learning, primarily by adding redundancy to gradient computations to mitigate the impact of slow or failing nodes ("stragglers"). Current research focuses on optimizing gradient coding for various distributed learning architectures, including federated learning and decentralized settings, exploring different coding schemes (e.g., 1-bit coding, hierarchical coding) and incorporating techniques like quantization and mixed-precision training to reduce communication overhead. These advancements are significant for accelerating large-scale machine learning tasks and enhancing the privacy and security of distributed systems, particularly in resource-constrained environments like edge computing.