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New research tackles wireless federated learning challenges

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore advancements in federated learning for wireless environments. The first paper introduces a convergence-latency-aware adaptive modulation and resource allocation scheme for RIS-assisted wireless federated learning, aiming to improve training speed and accuracy in challenging scenarios. The second paper proposes an online-score-aided federated learning algorithm designed for resource-constrained wireless clients with continually arriving data, addressing issues like data distribution shifts and limited storage. AI

IMPACT These papers propose new algorithms to improve the efficiency and effectiveness of federated learning in challenging wireless and resource-constrained environments.

RANK_REASON Two academic papers published on arXiv detailing new algorithms for federated learning in wireless environments.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research tackles wireless federated learning challenges

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Two academic papers published on arXiv detailing new algorithms for federated learning in wireless environments.
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COVERAGE [3]

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

    Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

    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) ·

    Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

    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 ·

    Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

    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…

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