HE‑OFT is a cryptographically secure one‑shot federated fine‑tuning protocol that protects privacy by exchanging only one encrypted contribution and never revealing the trained model. Each client fine‑tunes a low‑rank adapter and a classifier head on a frozen public backbone, keeps the adapter, and uploads an encrypted head displacement that the server combines using multiparty CKKS without decryption. On four text classification tasks and one vision task, HE‑OFT achieves 61–79 % accuracy, retaining 85–96 % of the accuracy of a disclosed model, while a test‑time query takes 443.1–1713.1 s on one core or 56.1–255.1 s on a GPU, with level restoration reducing traffic from 1.6 GiB to 13.5 MiB.
By Halil \.Ibrahim Kanpak, Sinem Sav, Alptekin K\"up\c{c}\"u
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
The paper introduces a new gradient inversion attack for federated learning that leverages concepts from erasure‑correcting codes to recover entire training batches and their labels from a single FedSGD round. Unlike previous analytic attacks, this method can exactly reconstruct batches of up to 128 samples on ImageNet and achieves over 90% recovery even when the attacker actively manipulates the model. The study demonstrates that federated learning’s privacy leakage is far greater than previously estimated.
By Saeed Shariati, Mohsen Alambardar Meybodi
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
The paper introduces FGLGuard, a privacy‑preserving federated graph learning framework that trains a graph attention detector on each operator’s own multi‑agent system (MAS) episode graphs, sharing only model updates. By combining a proximal local objective, domain‑balanced aggregation, threshold calibration, and guarded rewrite mechanisms, FGLGuard adapts to non‑IID data across organizations and outperforms centralized and local‑only baselines on Agent‑SafetyBench, R‑Judge, and AgentDojo. The method achieves significant reductions in attack success rates—up to 43% on AgentDojo—without compromising utility, API cost, or model capability.
By Jinxi Yu, Eric Hanchen Jiang, Levina Li, Dong Liu, Zhi Zhang, Wenxiao Zhao, Yanxuan Yu, Kai-Wei Chang, Ying Nian Wu
arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.
By Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi, Teddy Lazebnik