arXiv:2606. 25627v1 Announce Type: new Abstract: Distributed intelligent systems increasingly need to train across data silos without centralizing raw data.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2608. 15153v1 Announce Type: cross Abstract: Differentially private federated learning must balance privacy protection against model accuracy and training efficiency.
arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).
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.
arXiv:2503. 07570v3 Announce Type: replace-cross Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy.
arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.
arXiv:2609.37011v1 Announce Type: cross Abstract: Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into...