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In this work, we adopt the emerging technology of mobile edge computing (MEC)in the Unmanned aerial vehicles (UAVs) for communication-computing systems, tooptimize the age of information (AoI) in the network. We assume that tasks areprocessed jointly on UAVs and BS to enhance edge performance with limitedconnectivity and computing. Using UAVs and BS jointly with MEC can reduce AoIon the network. To maintain the freshness of the tasks, we formulate the AoIminimization in two-hop communication framework, the first hop at the UAVs andthe second hop at the BS. To approach the challenge, we optimize the problemusing a deep reinforcement learning (DRL) framework, called federatedreinforcement learning (FRL). In our network we have two types of agents withdifferent states and actions but with the same policy. Our FRL enables us tohandle the two-step AoI minimization and UAV trajectory problems. In addition,we compare our proposed algorithm, which has a centralized processing unit toupdate the weights, with fully decentralized multi-agent deep deterministicpolicy gradient (MADDPG), which enhances the agent's performance. As a result,the suggested algorithm outperforms the MADDPG by about 38\%

Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG