Dense: Data-free one-shot federated learning
Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …
which allows the central server to learn a model in a single communication round. Despite …
Delving into the adversarial robustness of federated learning
Abstract In Federated Learning (FL), models are as fragile as centrally trained models
against adversarial examples. However, the adversarial robustness of federated learning …
against adversarial examples. However, the adversarial robustness of federated learning …
Efficient federated learning for modern nlp
Transformer-based pre-trained models have revolutionized NLP for superior performance
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …
Federated few-shot learning for mobile nlp
Natural language processing (NLP) sees rich mobile applications. To support various
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …
Fedadapter: Efficient federated learning for modern nlp
Transformer-based pre-trained models have revolutionized NLP for superior performance
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …
Seqpate: Differentially private text generation via knowledge distillation
Protecting the privacy of user data is crucial for text generation models, which can leak
sensitive information during generation. Differentially private (DP) learning methods provide …
sensitive information during generation. Differentially private (DP) learning methods provide …
LLM-based privacy data augmentation guided by knowledge distillation with a distribution tutor for medical text classification
As sufficient data are not always publically accessible for model training, researchers exploit
limited data with advanced learning algorithms or expand the dataset via data augmentation …
limited data with advanced learning algorithms or expand the dataset via data augmentation …
Revisiting the random subset sum problem
A da Cunha, F d'Amore, F Giroire, H Lesfari… - arXiv preprint arXiv …, 2022 - arxiv.org
The average properties of the well-known Subset Sum Problem can be studied by the
means of its randomised version, where we are given a target value $ z $, random variables …
means of its randomised version, where we are given a target value $ z $, random variables …
On the multidimensional random subset sum problem
L Becchetti, ACW da Cunha, A Clementi… - arXiv preprint arXiv …, 2022 - arxiv.org
In the Random Subset Sum Problem, given $ n $ iid random variables $ X_1,..., X_n $, we
wish to approximate any point $ z\in [-1, 1] $ as the sum of a suitable subset $ X_ {i_1 (z)} …
wish to approximate any point $ z\in [-1, 1] $ as the sum of a suitable subset $ X_ {i_1 (z)} …
A Review of Federated Learning: Algorithms, Frameworks and Applications
In today's world, artificial intelligence (AI) and machine learning (ML) are being widely
adopted at an exponential rate. A key requirement of AI and ML models is data, which often …
adopted at an exponential rate. A key requirement of AI and ML models is data, which often …