Gradient Delay

Gradient delay, the lag between gradient computation and its use in model updates, is a critical challenge in distributed and federated learning, impacting algorithm efficiency and convergence. Current research focuses on mitigating the negative effects of delay through techniques like adaptive batch sizing, improved sampling schemes in asynchronous algorithms, and novel optimization methods that explicitly account for delayed gradients, often leveraging integer linear programming or multiple gradient descent approaches. Addressing gradient delay is crucial for optimizing the performance of large-scale machine learning systems, particularly in heterogeneous computing environments where computational speeds and communication latencies vary significantly.