2 citations · 2 across the 12 of their papers we have counts for
3 papers · 1 filter
RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1
Machine learning (ML) algorithms deployed in real-world environments are often faced with the challenge of adapting models to concept drift, where the task data distributions are s…
Device-Cloud Collaborative LLM Inference with Multi-Modal, Multi-Task, Multi-Turn Conversations
Liangqi Yuan, Dong-Jun Han, Shiqiang Wang +1
Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn con…
Correcting Before Adapting: Gradient Correction for Federated Adaptive Optimization
Evan Chen, Shiqiang Wang, Jianing Zhang +4
Client-drift compensation methods mitigate the impact of data heterogeneity in federated learning (FL). While drift correction is well studied for stochastic gradient descent (SGD)…