联邦学习与大语言模型交叉领域的最新综述，由PAKDD 2026接收，系统梳理进展并展望未来方向。
arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their …
联邦量子学习结合零噪声外推引导校正，有效提升噪声环境下量子模型的鲁棒性与性能。
arXiv:2605.30075v1 Announce Type: new Abstract: Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distribut…
联邦学习新突破：无需本地数据的客户端贡献评估，用Logit最大化实现精准量化，兼顾隐私与效率。
arXiv:2605.18892v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative learning of computer vision models, where privacy and re…
联邦学习中的激励感知聚合方法，在参与者策略行为下提供性能保证，解决数据共享激励难题
arXiv:2603.20873v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables…