Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning
研究使用ns-3模拟和FedAvg训练器估算802.11竞争下的联邦学习通信时间，通过帧传递率预测模型更新接纳概率，解决不同客户端密度和负载下的时间成本问题。
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FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
This work addresses the challenges of federated learning in low Earth orbit (LEO) satellite constellations, where non-IID data distributions and irregular ground station visibility can lead to aggregation failure or imbalanced personalization. To overcome these issues, the authors propose FedOrbit, a novel framework that uniquely integrates orbital geometry into federated learning design. FedOrbit enables orbit-level continuous training via inter-satellite links and introduces several key mechanisms: class-aware hierarchical aggregation, quality-weighted feature aggregation, backhaul-rate damping, and adaptive feature decomposition based on inter-orbit class similarity. Evaluated across three remote sensing benchmarks under two non-IID partitioning schemes, FedOrbit achieves state-of-the-art accuracy in five out of six settings—outperforming the strongest baseline by up to 16.1% (Dirichlet) and 8.6% (pathological)—with the remaining setting showing a marginal gap (<0.9%). It also substantially reduces performance disparity across orbits.
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Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
This work addresses the challenge of jointly optimizing cluster assignment and client selection in hierarchical federated learning under data heterogeneity. The authors propose Fed-BAC, the first framework integrating additive clustering personalization with a two-level bandit mechanism: at the cloud level, a contextual bandit dynamically assigns servers to clusters, while at the edge, Thompson Sampling selects high-contribution clients. Fed-BAC models both shared and cluster-specific patterns through a global network and cluster-adapted networks operating in tandem. Evaluated on three benchmarks including CIFAR-10, Fed-BAC significantly outperforms HierFAVG and IFCA, achieving accuracy gains of up to 35.5 and 8.4 percentage points, respectively, with only 80% client participation. It accelerates convergence by 1.5–4.8× and maintains effectiveness and fairness even when scaled up fivefold.
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An LLM -Powered Assessment Retrieval-Augmented Generation (RAG) For Higher Education
This study addresses the challenge of delivering timely, consistent, and high-quality feedback in large-scale higher education courses, where resource constraints often impede instructors’ capacity to provide individualized support. To this end, the authors propose a novel retrieval-augmented generation (RAG) system enhanced with pedagogical agent characteristics, representing the first application of a RAG architecture grounded in instructional logic to automated essay scoring. By integrating rubrics, exemplar essays, and historical feedback, the system generates context-aware scores and formative comments tailored to each student’s writing. Evaluation on a corpus of 701 student essays demonstrates strong alignment with human raters, achieving 94%–99% consistency. The approach significantly enhances the accessibility, consistency, and quality of feedback while effectively supporting students’ self-regulated learning.
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Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning
研究使用ns-3模拟和FedAvg训练器估算802.11竞争下的联邦学习通信时间，通过帧传递率预测模型更新接纳概率，解决不同客户端密度和负载下的时间成本问题。
0 citationsRead paper
FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
This work addresses the challenges of federated learning in low Earth orbit (LEO) satellite constellations, where non-IID data distributions and irregular ground station visibility can lead to aggregation failure or imbalanced personalization. To overcome these issues, the authors propose FedOrbit, a novel framework that uniquely integrates orbital geometry into federated learning design. FedOrbit enables orbit-level continuous training via inter-satellite links and introduces several key mechanisms: class-aware hierarchical aggregation, quality-weighted feature aggregation, backhaul-rate damping, and adaptive feature decomposition based on inter-orbit class similarity. Evaluated across three remote sensing benchmarks under two non-IID partitioning schemes, FedOrbit achieves state-of-the-art accuracy in five out of six settings—outperforming the strongest baseline by up to 16.1% (Dirichlet) and 8.6% (pathological)—with the remaining setting showing a marginal gap (<0.9%). It also substantially reduces performance disparity across orbits.
0 citationsRead paper
Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
This work addresses the challenge of jointly optimizing cluster assignment and client selection in hierarchical federated learning under data heterogeneity. The authors propose Fed-BAC, the first framework integrating additive clustering personalization with a two-level bandit mechanism: at the cloud level, a contextual bandit dynamically assigns servers to clusters, while at the edge, Thompson Sampling selects high-contribution clients. Fed-BAC models both shared and cluster-specific patterns through a global network and cluster-adapted networks operating in tandem. Evaluated on three benchmarks including CIFAR-10, Fed-BAC significantly outperforms HierFAVG and IFCA, achieving accuracy gains of up to 35.5 and 8.4 percentage points, respectively, with only 80% client participation. It accelerates convergence by 1.5–4.8× and maintains effectiveness and fairness even when scaled up fivefold.
0 citationsRead paper
An LLM -Powered Assessment Retrieval-Augmented Generation (RAG) For Higher Education
This study addresses the challenge of delivering timely, consistent, and high-quality feedback in large-scale higher education courses, where resource constraints often impede instructors’ capacity to provide individualized support. To this end, the authors propose a novel retrieval-augmented generation (RAG) system enhanced with pedagogical agent characteristics, representing the first application of a RAG architecture grounded in instructional logic to automated essay scoring. By integrating rubrics, exemplar essays, and historical feedback, the system generates context-aware scores and formative comments tailored to each student’s writing. Evaluation on a corpus of 701 student essays demonstrates strong alignment with human raters, achieving 94%–99% consistency. The approach significantly enhances the accessibility, consistency, and quality of feedback while effectively supporting students’ self-regulated learning.
0 citationsRead paper