Parallel Learning

Parallel learning aims to accelerate and improve machine learning by distributing computational tasks across multiple processors or devices. Current research focuses on optimizing this parallelism for various applications, including robotics, cybersecurity (e.g., in-vehicle intrusion detection), and autonomous driving, employing diverse model architectures like neural networks and employing techniques such as parameter servers and local learning to manage computational load and communication overhead. This approach is significant because it addresses the limitations of traditional sequential training methods, enabling faster model training and deployment, particularly for large datasets and complex models, while also improving energy efficiency in resource-constrained environments.