solve this problem, we propose a novel FL framework, namely FL with gradient recycling (FL-
GR), which recycles the historical gradients of unscheduled and transmission-failure devices
to improve the learning performance of FL. Based on the proposed FL-GR, we theoretically
analyze how the wireless network parameters affect the convergence bound of FL-GR,
revealing that scheduling devices with large staleness and increasing their transmit power in …