When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy

arXiv:2608.26655 · cs.LG · Submitted 2026-08-27 · Read on arXiv

cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 46× in this paper · explained in 5 episodes
• Alignment — 27× in this paper · explained in 1 episode
• Model Merging — 27× in this paper · explained in 2 episodes
• Benchmark — 20× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Differential Privacy — 17× in this paper · explained in 4 episodes
• Differentially Private — 15× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Differential Privacy (DP) — 3× in this paper · explained in 7 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Decoupled Weight Decay Regularization
• Opacus: User-Friendly Differential Privacy Library in PyTorch

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