Stale Global Model

Stale global models in asynchronous federated learning (AFL) represent a key challenge in distributed machine learning, where clients train on local data using outdated global model versions. Current research focuses on mitigating the negative impact of this staleness through adaptive aggregation techniques that weigh client updates based on factors like staleness and local training epochs, aiming to improve convergence speed and accuracy. These efforts are crucial for scaling federated learning to diverse and heterogeneous client environments, enabling more efficient and robust training of complex models across various applications.