Papers with privacy-sensitive applications
FedSpaLLM: Federated Pruning of Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing pruning methods assume public access to calibration data, which is impractical for privacy-sensitive applications. |
| Approach: | They propose a federated learning framework for pruning LLMs that prunes models locally based on private data while accounting for system heterogeneity and communication efficiency. |
| Outcome: | The proposed framework reduces communication overhead and personalizes pruning process based on client resources in federated settings. |
SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Existing privacy defenses reduce leakage of PII due to LLM memorization, but often degrade downstream performance. |
| Approach: | They propose a privacy-aware federated fine-tuning framework for large language models that provides fine-grained privacy control without sacrificing utility. |
| Outcome: | The proposed framework reduces PII leakage while providing fine-grained privacy control without sacrificing utility. |