38 citations · 39 across the 5 of their papers we have counts for
5 papers
: Hierarchical Information Extraction via Encoding and Embedding
Tianru Zhang, Li Ju, Prashant Singh +1
Analyzing large-scale datasets, especially involving complex and high-dimensional data like images, is particularly challenging. While self-supervised learning (SSL) has proven eff…
Deep Graph Reprogramming
Yongcheng Jing, Chongbin Yuan, Li Ju +3
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, with…
Accelerating Fair Federated Learning: Adaptive Federated Adam
Li Ju, Tianru Zhang, Salman Toor +1
Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data of different parties. However, when dataset…
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Shenghui Li, Edith Ngai, Fanghua Ye +3
Federated learning (FL) facilitates distributed training across different IoT and edge devices, safeguarding the privacy of their data. The inherent distributed structure of FL int…
Proactive Autoscaling for Edge Computing Systems with Kubernetes
Li Ju, Prashant Singh, Salman Toor
With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and…