Search Results for author: Luyang Liu

Found 15 papers, 5 papers with code

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

no code implementations12 Jan 2024 Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Gauri Joshi

Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs with on-device local data.

Federated Learning Privacy Preserving

GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning

no code implementations7 Jan 2024 Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang, Jian Liu

Specifically, we utilize constrained optimization to enforce local fairness on the client side and adopt a fairness-aware clustering-based aggregation on the server to further ensure the global model fairness across different sensitive groups while maintaining high utility.

Fairness Federated Learning

Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations

no code implementations2 Dec 2023 Neha Kalibhat, Warren Morningstar, Alex Bijamov, Luyang Liu, Karan Singhal, Philip Mansfield

We define augmentations in frequency space called Fourier Domain Augmentations (FDA) and show that training SSL models on a combination of these and image augmentations can improve the downstream classification accuracy by up to 1. 3% on ImageNet-1K.

Data Augmentation Self-Supervised Learning +1

RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense

no code implementations11 Apr 2023 Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses.

Adversarial Attack Attribute +4

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

3 code implementations3 Dec 2022 Samiul Alam, Luyang Liu, Ming Yan, Mi Zhang

Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical.

Federated Learning Model extraction

Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage

1 code implementation CVPR 2022 Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu

Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a central server without exchanging their private data.

Bayesian Optimization Federated Learning

Cross-Lingual Language Model Meta-Pretraining

no code implementations23 Sep 2021 Zewen Chi, Heyan Huang, Luyang Liu, Yu Bai, Xian-Ling Mao

The success of pretrained cross-lingual language models relies on two essential abilities, i. e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferring the task knowledge to other languages.

Cross-Lingual Transfer Language Modelling

Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates

no code implementations3 Jul 2021 Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

Federated Learning (FL) enables multiple distributed clients (e. g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client.

Federated Learning

Local Adaptivity in Federated Learning: Convergence and Consistency

no code implementations4 Jun 2021 Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi

Popular optimization algorithms of FL use vanilla (stochastic) gradient descent for both local updates at clients and global updates at the aggregating server.

Federated Learning

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

1 code implementation6 Jul 2020 Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, Shawn O'Banion

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.

Time Series Time Series Forecasting

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