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Found 12 papers, 7 papers with code
Position: LLM Unlearning Benchmarks are Weak Measures of Progress
Unlearning methods have the potential to improve the privacy and safety of large language models (LLMs) by removing sensitive or harmful information post hoc.
Jogging the Memory of Unlearned LLMs Through Targeted Relearning Attacks
Machine unlearning is a promising approach to mitigate undesirable memorization of training data in LLMs.
Privacy Amplification for the Gaussian Mechanism via Bounded Support
Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset.
Guardrail Baselines for Unlearning in LLMs
Recent work has demonstrated that finetuning is a promising approach to 'unlearn' concepts from large language models.
No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications.
Federated Learning as a Network Effects Game
Federated Learning (FL) aims to foster collaboration among a population of clients to improve the accuracy of machine learning without directly sharing local data.
On Privacy and Personalization in Cross-Silo Federated Learning
While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP and its implications for cross-silo FL, a setting characterized by a limited number of clients each containing many data subjects.
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Model compression is important in federated learning (FL) with large models to reduce communication cost.
Fair Federated Learning via Bounded Group Loss
In particular, we explore and extend the notion of Bounded Group Loss as a theoretically-grounded approach for group fairness.
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously.
Ditto: Fair and Robust Federated Learning Through Personalization
Fairness and robustness are two important concerns for federated learning systems.
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.
