Paper

PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems

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arXiv:2605. 16098v1 Announce Type: new Abstract: Federated learning (FL) is vulnerable to data poisoning attacks due to its distributed nature.

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Authors: Wei Sun, Yijun Chen, Bo Gao, Ke Xiong, Yuwei Wang, Pingyi Fan, Khaled Ben Letaief
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, eval, rate, compare, control, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.16098

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