3 papers
cs.LG2026
Why Does Train-Validation Separation Emerge? Update-Pressure Density Dynamics in Pretrained Backbones
Yuchen Li, Mingyu Du, Zongqi Fan +2
Train-validation separation is the evolving difference between performance on observed training examples and a finite held-out validation set. We propose a dynamic structural accou…
cs.LG2026
Routing in Gradient Space: Balanced Usage Is Not Expert Specialization
Yuchen Li, Mingyu Du, Zongqi Fan +2
Sparse expert models can distribute traffic evenly while still grouping incompatible training signals within the same experts. We study routing as a gradient-partitioning problem a…
cs.LG2026
Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression
Minh-Duong Nguyen, Senura Hansaja, Le-Tuan Nguyen +4
Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regul…