计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 59-65.doi: 10.11896/jsjkx.211000123
吴赟寒, 白光伟, 沈航
WU Yun-han, BAI Guang-wei, SHEN Hang
摘要： 考虑到车联网系统中多维资源消耗会随时间波动的特性和用户对高效计算服务以及数据隐私安全的需求,提出了一种基于联邦学习的车联网多维资源分配方法。一方面,综合考虑计算、缓存和带宽资源分配,保证计算任务的完成率,避免多维资源的冗余分配,基于该目标设计了一种深度学习算法,通过边缘服务器收集的数据预测各项资源的消耗量,以此为依据分配多维资源;另一方面,考虑到用户的数据隐私安全需求造成的数据孤岛问题,采用联邦学习架构以获得泛化性较好的神经网络模型。该算法能随时间调整多维资源的分配量,满足随时间变动的资源需求,保证车联网系统中计算任务的高效完成。实验结果表明该算法具有收敛速度快、模型泛化性好等特点,能以较少的通信轮数完成联邦学习的聚合。
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