Skip to yearly menu bar Skip to main content


Flash: Concept Drift Adaptation in Federated Learning

Kunjal Panchal · Sunav Choudhary · Subrata Mitra · Koyel Mukherjee · Somdeb Sarkhel · Saayan Mitra · Hui Guan

Exhibit Hall 1 #302
[ ]
[ PDF [ Poster


In Federated Learning (FL), adaptive optimization is an effective approach to addressing the statistical heterogeneity issue but cannot adapt quickly to concept drifts. In this work, we propose a novel adaptive optimizer called Flash that simultaneously addresses both statistical heterogeneity and the concept drift issues. The fundamental insight is that a concept drift can be detected based on the magnitude of parameter updates that are required to fit the global model to each participating client's local data distribution. Flash uses a two-pronged approach that synergizes client-side early-stopping training to facilitate detection of concept drifts and the server-side drift-aware adaptive optimization to effectively adjust effective learning rate. We theoretically prove that Flash matches the convergence rate of state-of-the-art adaptive optimizers and further empirically evaluate the efficacy of Flash on a variety of FL benchmarks using different concept drift settings.

Chat is not available.