Xin Xie

Exploring Amplified Heterogeneity Arising From Heavy-Tailed Distributions in Federated Learning

2024 · IEEE Transactions on Mobile Computing

Federated Learning (FL) has emerged as a privacy-preserving paradigm enabling collaborative model training among distributed clients. However, current FL methods operate under the closed-world assumption, i.e., all local training data originates from a global labeled …

Document-level Relation Extraction as Semantic Segmentation

2021

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the …

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

2022 · Proceedings of the ACM Web Conference 2022

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language …