Decentralized Network

Decentralized networks aim to distribute computation and data storage across multiple nodes without relying on a central server, enhancing robustness, privacy, and scalability. Current research focuses on developing efficient algorithms for decentralized machine learning, including variations of stochastic gradient descent and ADMM, often incorporating techniques like asynchronous updates and gossip protocols to handle communication constraints and potential adversarial nodes. These advancements are improving the efficiency and reliability of distributed computation for applications ranging from social media moderation and IoT device coordination to large-scale optimization problems in various fields.