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Jinqiao Shi

5 accepted papers

2026

Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation

AAAI 2026technical

Federated learning (FL) allows for collaborative model training while preserving data privacy, but its distributed nature makes it vulnerable to poisoning attacks. Existing defense methods typically rely on using gradients from multiple clients to define a trusted region, selecting only the trustwor

Cited by 0SourcePDFScholar
2023

URM4DMU: An User Representation Model for Darknet Markets Users

ICASSP 2023accepted

Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on different markets makes it easy to aggregate user information across different platforms, which helps identify anonymous u…

Cited by 0SourceScholar
2022

Document-Level Event Extraction via Human-Like Reading Process

ICASSP 2022accepted

Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of one event record could reside in different sentences in the document, while the second one reflects that one document ma…

Cited by 0SourceScholar
2022

Event Causality Extraction with Event Argument Correlations

COLING 2022main

Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it s…