
The proposed FLARE framework allows participating devices to dynamically adjust their individual learning rates and local training iterations based on their instantaneous computing powers, mitigating the impact of device and data heterogeneity in wireless federated learning.


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federated-learning-with-dynamically-adjusted-learning-rates-for-resource-constrained-wireless-networks


Federated Learning with Dynamically Adjusted Learning Rates for Resource-Constrained Wireless Networks
