2 citations · 3 across the 7 of their papers we have counts for
7 papers
Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback
Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li +1
Distributed learning, particularly Federated Learning (FL), faces a significant bottleneck in the communication cost, particularly the uplink transmission of client-to-server updat…
Communication Compression for Distributed Learning without Control Variates
Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li +1
Distributed learning algorithms, such as the ones employed in Federated Learning (FL), require communication compression to reduce the cost of client uploads. The compression metho…
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang +1
Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless n…
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
Ruinan Jin, Chun-Yin Huang, Chenyu You +1
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, th…
Scalable Data Point Valuation in Decentralized Learning
Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh +3
Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically d…
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
Chun-Yin Huang, Ruinan Jin, Can Zhao +2
While Federated Learning (FL) is gaining popularity for training machine learning models in a decentralized fashion, numerous challenges persist, such as asynchronization, computat…
Efficient Medical Image Assessment via Self-supervised Learning
Chun-Yin Huang, Qi Lei, Xiaoxiao Li
High-performance deep learning methods typically rely on large annotated training datasets, which are difficult to obtain in many clinical applications due to the high cost of medi…