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Found 7 papers, 1 papers with code
Partial Federated Learning
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
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.
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.
Federated Learning with Noisy User Feedback
Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.
Robust Correlation Clustering with Asymmetric Noise
Graph clustering problems typically aim to partition the graph nodes such that two nodes belong to the same partition set if and only if they are similar.
Provable Overlapping Community Detection in Weighted Graphs
When the communities are allowed to overlap, often a pure nodes assumption is made, i. e. each community has a node that belongs exclusively to that community.
