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English(EN) Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

新研究应对无线联邦学习挑战

两篇新研究论文探讨了无线环境下的联邦学习进展。第一篇论文提出了一种面向RIS辅助无线联邦学习的收敛-延迟感知自适应调制与资源分配方案,旨在提高在挑战性场景下的训练速度和准确性。第二篇论文提出了一种在线分数辅助联邦学习算法,专为具有持续到达数据的资源受限无线客户端设计,解决了数据分布变化和存储有限等问题。 AI

影响 这些论文提出了新算法,以提高联邦学习在挑战性无线和资源受限环境中的效率和有效性。

排序理由 两篇在arXiv上发表的学术论文,详细介绍了无线环境下联邦学习的新算法。

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新研究应对无线联邦学习挑战

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两篇在arXiv上发表的学术论文,详细介绍了无线环境下联邦学习的新算法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Liwei Wang, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xusheng Zhu, Qiong Wu ·

    RIS辅助无线联邦学习中的收敛-延迟感知自适应调制与资源分配

    arXiv:2607.19759v1 Announce Type: cross Abstract: Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    RIS辅助无线联邦学习中的收敛-延迟感知自适应调制与资源分配

    Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communicat…

  3. arXiv cs.LG TIER_1 English(EN) · Ferdous Pervej, Minseok Choi, Andreas F. Molisch ·

    面向资源受限的无线客户端及持续数据到达的在线评分辅助联邦学习

    arXiv:2408.05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as fed…