13 citations · 18 across the 9 of their papers we have counts for
6 papers · 1 filter
Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
Yue Niu, Zhaokai Sun, Jiayi Yang +5
Despite their success in various domains, the growing dependence on GNNs raises a critical concern about the nature of the combinatorial reasoning underlying their predictions, whi…
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
Lei Gao, Amir Ziashahabi, Yue Niu +2
Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned w…
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
Sunwoo Lee, Tuo Zhang, Saurav Prakash +2
In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space. To implement large-scale FL applications, thus, it…
Edge Private Graph Neural Networks with Singular Value Perturbation
Tingting Tang, Yue Niu, Salman Avestimehr +1
Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN trainin…
ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys
Yue Niu, Saurav Prakash, Salman Avestimehr
We propose a new attention mechanism with linear complexity, ATP, that fixates \textbf{A}ttention on \textbf{T}op \textbf{P}rincipal keys, rather than on each individual token. Par…
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Sara Babakniya, Souvik Kundu, Saurav Prakash +2
Edge devices can benefit remarkably from federated learning due to their distributed nature; however, their limited resource and computing power poses limitations in deployment. A…