Decentralized Deep Learning

Decentralized deep learning focuses on training deep neural networks across multiple devices or nodes without a central server, aiming to improve efficiency, privacy, and robustness. Current research emphasizes addressing challenges like heterogeneous data distributions and high communication costs through novel algorithms such as sharpness-aware minimization, iterative merging-and-training, and adaptive consensus step-size adjustments, often incorporating techniques like momentum and communication compression. This field is significant for enabling large-scale training with privacy-preserving data handling and facilitating applications in resource-constrained environments like edge computing and federated learning.