15 citations · 40 across the 15 of their papers we have counts for
7 papers · 1 filter
Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling
Seyyed Saeid Cheshmi, Azal Ahmad Khan, Xinran Wang +2
Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks in domains such as mathematics, logic, and multi-step question answering. A growing li…
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation
Qi Le, Enmao Diao, Xinran Wang +3
Federated Learning (FL) is a collaborative machine learning framework that allows multiple users to train models utilizing their local data in a distributed manner. However, consid…
ColA: Collaborative Adaptation with Gradient Learning
Enmao Diao, Qi Le, Suya Wu +4
A primary function of back-propagation is to compute both the gradient of hidden representations and parameters for optimization with gradient descent. Training large models requir…
A Framework for Incentivized Collaborative Learning
Xinran Wang, Qi Le, Ahmad Faraz Khan +2
Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that can…
IP-FL: Incentivized and Personalized Federated Learning
Ahmad Faraz Khan, Xinran Wang, Qi Le +7
Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personaliza…
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Ahmad Faraz Khan, Yuze Li, Xinran Wang +5
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT ap…
Assisted Learning: A Framework for Multi-Organization Learning
Xun Xian, Xinran Wang, Jie Ding +1
In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organiza…