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
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
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. 25858v1 Announce Type: cross Abstract: Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance.
By Kavindu Herath, Joshua C. Zhao, Saurabh Bagchi
arXiv:2607. 19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches that lack semantic understanding, making them vulnerable to stealthy triggers and harmful to benign structures.
By Chenyu Zhou, Yabin Peng, Wei Huang, Kunlin Li, Shuaishuai Zhang, Xinyuan Miao
arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).
By Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini
DecoyTrace is a proactive cyber‑deception defense designed for strictly serverless decentralized federated learning (DFL). It deploys mobile DecoyNodes that generate chaotic decoy challenges, disseminate dual models (clean vs. decoy) based on neighbor trust, and use three‑state semantic metrics to isolate malicious sources and recover models. Across sixty configurations on the NEBULA platform, DecoyTrace restores model utility with minimal performance loss and reduces CPU and network usage by up to two‑thirds.
By Pedro Beltr\'an-L\'opez, Enrique Tom\'as Mart\'inez Beltr\'an, Pantaleone Nespoli, Manuel Gil P\'erez, Alberto Huertas Celdr\'an
arXiv:2609.40312v1 Announce Type: new
Abstract: Lossy compression is widely used in Federated Learning (FL) but is generally treated as an error source, while conventional poisoning defenses inspect...
By Sachi Shome, William Eiers
arXiv:2608. 14861v1 Announce Type: cross Abstract: Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision.
By Nawrin Tabassum, Yanzhao Wu
arXiv:2609.37367v1 Announce Type: cross
Abstract: Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a centra...
By Sayan Biswas, Jade Garcia Bourr\'ee, Rachid Guerraoui, Maxime Jacovella, Anne-Marie Kermarrec, Sathwika Peechara, Martijn de Vos, Milos Vujasinovic
arXiv:2601. 07177v5 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs).
By Mingxiang Tao, Yu Tian, Wenxuan Tu, Yue Yang, Xue Yang, Xiangyan Tang
The paper introduces FedMAST, a Federated Multi‑Axis Structural Tracing defense designed to detect and contain backdoor attacks in federated learning. FedMAST evaluates client updates through complementary structural, spectral, and historical evidence, applying tiered filtering and round‑level containment. In experiments across six backdoor attacks, FedMAST consistently achieves lower attack success rates while preserving high main‑task accuracy.
By Srinivasan Subramanian, Kazi Aminul Islam, Md. Abdullah Al Hafiz Khan