5 papers
Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computing
Shengyuan Ye, Bei Ouyang, Tianyi Qian +6
Large language models (LLMs) have unlocked a plethora of powerful applications at the network edge, such as intelligent personal assistants. Data privacy and security concerns have…
Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices
Weiheng Tang, Jingyi Li, Lin Chen +1
Edge computing has recently emerged as a promising paradigm to boost the performance of distributed learning by leveraging the distributed resources at edge nodes. Architecturally,…
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
Guangjing Huang, Qiong Wu, Jingyi Li +1
Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the…
Chained-DP: Can We Recycle Privacy Budget?
Jingyi Li, Guangjing Huang, Liekang Zeng +2
Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inj…
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks
Jingyi Li, Guocheng Liao, Lin Chen +1
Deep learning classifiers are crucial in the age of artificial intelligence. The device-edge-based collaborative inference has been widely adopted as an efficient framework for pro…