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Wenjie Fu

4 accepted papers

2026

FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting

AAAI 2026technical

In real-world time-series modelling, graph structures are widely adopted because they explicitly encode node topology and capture complex network dynamics. In practice, however, a complete graph is often partitioned across multiple parties; each party can access only its local sub-graph and, owing t

Cited by 0SourcePDFScholar
2026

Rethinking LLM Evaluation: Can We Evaluate LLMs with 200× Less Data?

ICLR 2026poster

As the demand for comprehensive evaluations of diverse model capabilities steadily increases, benchmark suites have correspondingly grown significantly in scale. Despite notable advances in redundancy reduction and subset-level performance prediction, a systematic framework that effectively integrat…

Cited by 0SourcecodeScholar
2025

MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector

AAAI 2025technical

The increasing parameters and expansive dataset of large lan- guage models (LLMs) highlight the urgent demand for a technical solution to audit the underlying privacy risks and copyright issues associated with LLMs. Existing studies have partially addressed this need through an exploration of the pr…

2024

Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

NeurIPS 2024poster

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear prom…