用低秩残差压缩给分割学习“减负”，通信开销大减，隐私与效率兼得的新思路。
arXiv:2608.23018v1 Announce Type: cross Abstract: Federated fine-tuning of on-device large language models (LLMs) faces a significant computing burden…
突破带宽限制，DiLoCo类方法如何平衡通信与性能，提升LLM预训练效率
arXiv:2508.15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.g., DiLoCo) have received considerable …
Fisher信息引导的token量化新方法，让边缘设备上的大模型联邦微调通信开销大幅缩减。
arXiv:2604.25421v2 Announce Type: replace-cross Abstract: Federated fine-tuning provides a practical route to adapt large language models (LLMs) on ed…
提出一种压缩模型大小与类别数双重带宽瓶颈的联邦蒸馏方法，大幅提升通信效率。
arXiv:2607.00173v1 Announce Type: new Abstract: Federated learning is bandwidth-bound on two orthogonal axes: model size, which limits how often param…
后训练阶段通过形成通信惯例来提升效率，COLM 2025接收论文，揭示多智能体协作新范式。
arXiv:2508.06482v2 Announce Type: replace-cross Abstract: Humans communicate with increasing efficiency in multi-turn interactions, by adapting their …
数字孪生为工业物联网异常检测装上联邦学习引擎，攻克通信效率与隐私瓶颈。
arXiv:2601.01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, …
这篇论文提出统一本地通信与更新策略，旨在提升大模型预训练的通信效率，分布式训练的新视角。
arXiv:2606.11081v1 Announce Type: cross Abstract: Communication-efficient pre-training of LLMs is increasingly important as training draws on compute …
创新性负载均衡与通信优化，FlashCP打破LLM长上下文训练效率瓶颈。
arXiv:2606.08476v1 Announce Type: cross Abstract: Context parallelism (CP) is essential for training large-scale, long-context language models, as it …
通过不确定性感知的机会传输与压缩，大幅降低混合语言模型中设备端与云端之间的通信开销。
arXiv:2505.11788v2 Announce Type: replace-cross Abstract: To support emerging language-based applications using dispersed and heterogeneous computing …