The paper introduces Fed-ADR, a coordinated attack framework where a malicious orchestrator server directs heterogeneous adversarial clients to adapt their gradient updates in real time, thereby evading existing federated learning defenses and drastically reducing global model accuracy. It also presents a lightweight detection mechanism that estimates true client gradients from historical data to spot coordinated attacks, and an in-situ recovery method that restores model performance without restarting training. Experiments on MNIST, Fashion‑MNIST, and CIFAR‑10 show the attack can drop accuracy from over 90% to below 10%, while the defense can recover accuracy to above 90% within a few rounds at a computational cost at least 20× lower than retraining from scratch.
By Mohamed Shaaban, Ahmed Abdelnaby, Mohamed Elmahallawy
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
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: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 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:2610.20264v1 Announce Type: new
Abstract: Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minim...
By Tiantong Sun, Jinlin Liu, Xingru Wei, Zequan Fang, Wei Chen, Qiang Yang, Xiaojin Zhang
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
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
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
FedGraph is a graph-based aggregation framework designed to defend federated large language model fine-tuning against backdoor attacks. It exploits the observation that malicious clients produce consistently similar updates, while benign clients generate diverse ones, by modeling client updates as nodes in a dynamically constructed graph and extracting structural features. The resulting compact client representations enable effective identification and suppression of malicious contributions, achieving high detection accuracy and low false positives in extensive experiments.
By Xi Chen, Chunyi Zhou, Rui Zeng, Xiaogang Xu, Zhe Liu, Shouling Ji
arXiv:2606. 17035v1 Announce Type: new Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks.
By Xiaolin Li, Ning Wang, Ninghui Li, Wenhai Sun
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