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. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
By Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
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
arXiv:2506.20651v1 Announce Type: cross
Abstract: Recent work has shown that gradient updates in federated learning (FL) can unintentionally reveal sensitive information about a client's local data....
By Fei Wang, Baochun Li
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 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
The paper proposes using lossy compression as a security signal in federated learning by defining a "compression footprint"—a low‑dimensional set of statistics derived from the compressor’s output. It introduces CRAFT, a server‑side aggregation method that leverages these footprints to distinguish honest from malicious updates without extra client metadata or knowledge of attacker numbers. Experiments on six attacks and datasets show CRAFT often outperforms existing robust aggregation baselines, demonstrating that compression can simultaneously reduce communication and enhance security.
By Sachi Shome, William Eiers
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization proposes a new framework that improves deepfake detection and interpretability. The approach introduces Feature-robust Augmentation—diversified degradation-aware strategies combined with supervised contrastive learning and a mean-teacher architecture—to maintain accuracy on low-quality images. For explanations, it employs evidence-grounded preference optimization, guiding the model to focus on genuine manipulation traces by learning from chosen-rejected explanation pairs that omit evidence or inject irrelevant details. The method achieved first place in the ACM Multimedia 2026 Explainable Deepfake Detection Challenge and is publicly available on GitHub.
By Zhu Xu, Jiaqi Tang, Pokai Chen, Yuxin Peng, Yang Liu
The paper investigates privacy and integrity risks in federated fine‑tuning of a pretrained MobileNetV4 model on Quick, Draw! data. It shows that a single client update can reveal private labels, that label‑count recovery degrades with more clients, and that backdoor and label‑flipping attacks can succeed under FedAvg but are mitigated by FLTrust. The study also demonstrates a live poisoning session using Indigma’s FedMaestro orchestrator.
By Vasileios Perifanis, Nikolaos Pavlidis
arXiv:2607. 07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself.
By Chongkai Li, Bang Zhang, Wenjian Luo
arXiv:2606. 10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning.
By Huong Nguyen, Micka\"el Bettinelli, Amirhossein Ghaffari, Alexandre Benoit, Hong-Tri Nguyen, Susanna Pirttikangas, Lauri Lov\'en
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