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Found 13 papers, 3 papers with code
Coordinated Replay Sample Selection for Continual Federated Learning
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history.
FLIRT: Feedback Loop In-context Red Teaming
Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs.
Differentially Private Decoding in Large Language Models
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart.
Canary Extraction in Natural Language Understanding Models
In this work, we present a version of such an attack by extracting canaries inserted in NLU training data.
Learnings from Federated Learning in the Real world
In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL.
An Efficient DP-SGD Mechanism for Large Scale NLP Models
However, the data used to train NLU models may contain private information such as addresses or phone numbers, particularly when drawn from human subjects.
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.
ADePT: Auto-encoder based Differentially Private Text Transformation
We prove the theoretical privacy guarantee of our algorithm and assess its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al., 2017) on models trained with transformed data.
Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results.
Learning Determinantal Point Processes in Sublinear Time
We propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items.
Decentralized Topic Modelling with Latent Dirichlet Allocation
Privacy preserving networks can be modelled as decentralized networks (e. g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node.
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods.
