[2603.01040] Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

Computer Science > Machine Learning arXiv:2603.01040 (cs) [Submitted on 1 Mar 2026] Title:Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift Authors:Heewon Park, Mugon Joe, Miru Kim, Kyungjin Im, Minhae Kwon View a PDF of the paper titled Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift, by Heewon Park and 4 other authors View PDF HTML (experimental) Abstract:Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-truth labels. A major challenge is learning rate selection under client-specific, time-varying distribution shifts, where fixed learning rates often lead to underfitting or divergence. We propose Fed-ADE (Federated Adaptation with Distribution Shift Estimation), an unsupervised federated adaptation framework that leverages lightweight estimators of distribution dynamics. Specifically, Fed-ADE employs uncertainty dynamics estimation to capture changes in predictive uncertainty and representation dynamics estimation to detect covariate-level feature drift, combining them into a per-client, per-timestep adaptive learning rate. We provide theoretical analyses showing that our dynamics estimation approximates the underlying distribution shift and yields dynamic regret and convergence guarantees. Experiments on image and text benchmarks under diverse distribution shifts (label and covariate) demonstrate co...