Understanding Infrastructure Drift in Federated Learning Systems

In traditional distributed machine learning (ML) systems, infrastructure drift refers to the deviation of underlying computational resources and network conditions over time, leading to reduced system performance and model accuracy. Infrastructure drift can arise from human input, poor configuration, or applications making unwanted changes. As ML systems evolve into a decentralized framework with the need to keep data private, Federated Learning (FL) systems have risen.