FedLNS is a server‑side framework that screens federated learning updates by representing each client’s contribution through changes in trainable normalization‑layer parameters, creating lightweight signatures that can be compared against a history‑aware cross‑client reference. The method requires no extra client‑to‑server communication, raw data, or labeled attack examples, and after screening, the remaining full‑model updates are aggregated with standard federated learning rules. Experiments on GPT‑style, BERT‑style, and LLaMA‑style models with 200 clients demonstrate that FedLNS achieves lower test perplexity than six baselines even when 40% of the population performs target manipulation under both IID and non‑IID data partitions.
By Kai Li, Jong-Ik Park, Carlee Joe-Wong, Wei Ni, Falko Dressler
arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.
By Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore, Stefano Zanero, Stefano Longari, Michele Carminati
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.
By Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul Islam
arXiv:2608.21137v1 Announce Type: new
Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...
By Mouhamed Amine Bouchiha, Gregory Blanc, Yufei Han
arXiv:2606. 09548v1 Announce Type: cross Abstract: Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data.
By Bastien Vuillod, Kevin Hector, Pierre-Alain Moellic, Jean-Max Dutertre, Olivier Potin
FedNIA is a defense framework for federated learning that identifies and excludes malicious clients without needing a central test dataset. It works by injecting random noise inputs and analyzing layerwise activation patterns with an autoencoder to detect abnormal behaviors caused by data poisoning. The method can counter various attack types—including sample poisoning, label flipping, and backdoors—even when multiple attackers collaborate, and shows strong performance on non‑iid federated datasets.
By Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses.
The paper introduces FedIoC, a federated learning framework that embeds structured threat indicators into gradient updates using a supervised contrastive loss. By aligning gradients from clients that share indicators for the same attack campaign, the server can cluster updates via cosine similarity to recover global campaign patterns without transmitting sensitive indicators. Experiments on two public threat‑detection benchmarks show that the server successfully identifies cross‑organizational campaign cohorts from fragmented local data.
By Manuel R\"oder, Bibin Babu, Frank-Michael Schleif
arXiv:2606. 15441v1 Announce Type: cross Abstract: Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution.
By Lipeng He, Yihan Wang, Jiawen Zhang, N. Asokan
The paper investigates how adversarial examples transfer between client models in federated learning and explores the relationship between these examples and client data distributions. It proposes a defense strategy based on adversarial training that leverages the transferability of model robustness. Experiments on real-life datasets demonstrate that the new attack and defense methods outperform existing state‑of‑the‑art approaches.
By Zuobin Xiong, Deval Mukherjee, Homook Cho, Wei Li
arXiv:2604. 04611v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data.
By Motoki Nakamura