6 citations · 6 across the 2 of their papers we have counts for
6 papers
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection
Desong Zhang, Jia Hu, Geyong Min
Spiking Neural Networks (SNNs) process information via discrete spikes, enabling them to operate at remarkably low energy levels. However, our experimental observations reveal a st…
Zero-Knowledge Proof-Based Consensus for Blockchain-Secured Federated Learning
Tianxing Fu, Jia Hu, Geyong Min +1
Federated learning (FL) enables multiple participants to collaboratively train machine learning models while ensuring their data remains private and secure. Blockchain technology f…
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge
Songyuan Li, Jia Hu, Geyong Min +1
Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federated Learning (SFL) facilitates pr…
Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market
Songyuan Li, Jia Hu, Geyong Min +2
The convergence of edge computing and Artificial Intelligence (AI) gives rise to Edge-AI, which enables the deployment of real-time AI applications at the network edge. A key resea…
Cicada: A Pipeline-Efficient Approach to Serverless Inference with Decoupled Management
Z. Wu, Y. Deng, J. Hu +4
Serverless computing has emerged as a pivotal paradigm for deploying Deep Learning (DL) models, offering automatic scaling and cost efficiency. However, the inherent cold start pro…
Federated Continual Learning for Edge-AI: A Comprehensive Survey
Zi Wang, Fei Wu, Feng Yu +3
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge,…