Asynchronous Decentralized

Asynchronous decentralized methods aim to enable collaborative model training across distributed networks without a central server or strict synchronization, improving efficiency and robustness. Current research focuses on developing algorithms like asynchronous stochastic gradient descent and federated learning variants that handle variable communication delays and device heterogeneity, often employing gossip protocols or randomized model averaging. These advancements are significant for applications requiring privacy-preserving data analysis, such as medical diagnosis, and for improving the scalability and resilience of distributed machine learning systems in diverse settings, including robotics and IoT networks.