Xin Wei

Optimize Scheduling of Federated Learning on Battery-powered Mobile Devices

2020

Federated learning learns a collaborative model by aggregating locally-computed updates from mobile devices for privacy preservation. While current research typically prioritizing the minimization of communication overhead, we demonstrate from an empirical study, that computation heterogeneity …

Enhancing Semi-Supervised Learning with Cross-Modal Knowledge

2022 · Proceedings of the 30th ACM International Conference on Multimedia

Semi-supervised learning (SSL), which leverages a small number of labeled data that rely on expert knowledge and a large number of easily accessible unlabeled data, has made rapid progress recently. However, the information comes from …

Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning

2024 · arXiv (Cornell University)

Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic. The field, however, grapples with issues such as outcome variability, laborious processes, and a scarcity of …

Compression Meets Security: Low-Complexity Linear Collaborative Federated Learning With Enhanced Accuracy

2025 · IEEE Transactions on Mobile Computing

Federated learning (FL) has been regarded as a promising paradigm for enabling distributed model training over resource-limited edge devices. Although FL maintains data locality and enhances model generalization, it faces challenges such as model leakage …