FedEach: Federated Learning with Evaluator-Based Incentive Mechanism for Human Activity Recognition by Hyun Woo Lim, Sean Yonathan Tanjung, Ignatius Iwan, Bernardo Nugroho Yahya, Seok-Lyong Lee

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Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection by Saeedeh Ghazi, Saeed Farzi, Amirhossein Nikoofard

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Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT by Rana Albelaihi

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An Intelligent Client Selection Algorithm of Federated Learning for Class-imbalance by ZHU Suxia, WANG Yunmeng, YAN Peisen, SUN Guanglu

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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

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Fairness in Federated Learning: Trends, Challenges, and Opportunities by Noorain Mukhtiar, Adnan Mahmood, Quan Z. Sheng

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Enhancing Communication Efficiency in Federated Learning Through Client Contribution Selection Based Normalized L2 Norm with Cosine Similarity and L2 Norm Filtering by Goran Saman Nariman, Hozan Khalid Hamarashid

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Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach by Zohra Dakhia, Massimo Merenda

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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

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Adaptive Federated Learning With Reinforcement Learning-Based Client Selection for Heterogeneous Environments by Shamim Ahmed, M. Shamim Kaiser, Sudipto Chaki, A. B. M. Shawkat Ali

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Eecs-fl: energy-efficient client selection for federated learning in AIoT by Yiyang Zhang, Yiming Luo, Tao Yang, Xiaofeng Wu, Bo Hu

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