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Miru Kim

2 accepted papers

2026

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

CVPR 2026

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 lea

Cited by 2SourcecodeScholar
2026

Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing

ICML 2026poster

Model merging has become a practical post-training strategy for building a single multi-task large language model (LLM) by combining multiple task-specialized models, avoiding costly joint training. However, most existing approaches rely on post-hoc merging, in which task-specific models are merged …

Cited by 0SourceScholar