Xiaoxiao Li

Efficient Medical Image Assessment via Self-supervised Learning

2022 · arXiv (Cornell University)

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 medical image labeling. Existing data assessment methods commonly require …

FedMT: Federated Learning with Mixed-type Labels

2022 · arXiv (Cornell University)

In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple data centers without exchanging data across them, which improves the sample efficiency. However, the conventional FL setting assumes the same labeling …

Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks

2023 · IEEE Journal of Biomedical and Health Informatics

U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated learning (FL) …

Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning

2024 · arXiv (Cornell University)

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many existing AFL methods focus on providing decision support to …

Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning

2025 · arXiv (Cornell University)

Reinforcement learning with verifiable rewards has significantly advanced the reasoning capabilities of large language models, yet how to explicitly steer training toward exploration or exploitation remains an open problem. We introduce Token Hidden Reward (THR), …