2 papers
cs.LG2026
Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
Heewon Park, Mugon Joe, Miru Kim +2
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…
cs.LG2025
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
Heewon Park, Mugon Joe, Miru Kim +1
In real-world applications, machine learning models face online label shift, where label distributions change over time. Effective adaptation requires careful learning rate selecti…