arXiv AI By C\^ome-Alexis Puech, S\'ebastien Thuau, Amira Gran, Arthur Mennessier, Siba Haidar, Rachid Chelouah

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

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arXiv AI
Aug 26

STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning

The paper introduces STAIN-FL, a stealthy backdoor attack framework for federated video anomaly detection that uses natural surveillance conditions—such as low light, indoor settings, and crowd density—as contextual triggers. STAIN-FL manipulates anomaly labels and masks gradients to keep clean accuracy low while inducing trigger‑conditioned misclassification. Experiments on UCF‑Crime with I3D features show that sparse attacks remain undetectable, drop clean accuracy by less than 2%, yet achieve over 50% backdoor accuracy for hundreds of rounds under FedAvg and FedProx.

By Ashlinder Kaur, Purnima Murali Mohan, Zengxiang Li, Tram Truong-Huu
arXiv AI
4d ago

Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection

The paper proposes a lightweight federated multiple‑instance learning (MIL) framework that trains only a compact MIL scorer across distributed clients while using a frozen vision‑language model (VLM) to verify high‑scoring video segments post‑hoc. Two VLM feedback interfaces are explored: a parsed text‑generation interface and a logit‑based interface that derives a continuous anomaly score from next‑token Yes/No probabilities. Experiments on UCF‑Crime with InternVL3.5‑2B and Qwen3‑VL‑2B‑Instruct show that the logit interface consistently improves frame‑level AUC and AP over the MIL baseline without requiring temporal post‑processing, whereas the text‑generation interface is more sensitive to prompts, parsers, and model choice.

By S\'ebastien Thuau, Amira Gran, Siba Haidar, Rachid Chelouah
arXiv Machine Learning
Aug 28

Federated Adversarial Training with Transformers

The paper investigates federated adversarial training (AT) for vision transformers, a topic not previously explored in federated learning (FL). It evaluates various transformer architectures and aggregation strategies, and introduces FedWAvg, an extension of FedAvg that weights client updates based on similarity of their last-layer representations. Experiments demonstrate that FedWAvg yields higher robust accuracy than existing aggregation methods in non‑IID settings.

By Ahmed Aldahdooh, Wassim Hamidouche, Olivier D\'eforges