Author

Zhe Liu

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#artificial intelligence Preprint Oct 2026

FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation

Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits. However, this setting is highly vulnerable to backdoor attacks, in which malicious clients manipulate their updates to induce targeted behaviors while...

Xi Chen, Chun-Yi Zhou, Rui Zeng et al. · 0 citations

Preprint Oct 2026

LDPGraph: Locally Differentially Private Graph Synthesis by Exploiting Neighborhood Structure

The widespread application of graph data inevitably brings significant privacy risks, as its unprotected use can lead to the leakage of sensitive information. These risks are particularly acute in the setting with an untrusted curator, where the data remains decentralized and each user only holds the connections to the...

Jia-Wei Dong, Zhi-Kun Zhang, Quan Yuan et al. · 0 citations

2026

PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning

Existing Federated Learning (FL) backdoor attacks commonly employ round-wise proximity strategies, dynamically adapting malicious updates to mimic benign ones in order to evade detection. However, such adaptive mechanisms often introduce instability, increase computational overhead, and create temporal patterns that ma...

Xi Chen, Rui Zeng, Chun-Yi Zhou et al. · IEEE Transactions on Informa... · 0 citations