计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 53-58.doi: 10.11896/jsjkx.220700136
程帆, 王瑞锦, 张凤荔
CHENG Fan, WANG Rui-jin, ZHANG Feng-li
摘要： 边缘计算(Edge Computing)作为一种新的计算范式,在网络边缘提供计算服务,相比传统的云计算模式,它具有高可信、低延迟等特点,在各行各业中有着广阔的应用前景,但在隐私保护和数据处理上仍存在一些问题。而联邦学习作为一种分布式的机器学习技术,能很好地解决边缘计算场景下数据分布不一致和数据隐私问题,但仍面临设备异构、数据异质及通信方面的挑战,如模型偏移、收敛效果差、部分设备计算结果丢失等问题。为解决上述问题,提出动态权重的联邦学习优化算法(FedDw)。该算法关注设备的服务质量,减少训练速度不一致导致部分设备参与带来的异构性影响,并根据服务质量确定在最终模型聚合时的占比,从而确保聚合的结果在复杂的真实情况下更具有鲁棒性。在10个地区气象站的真实数据集上与FedProx和Scaffold这两种典型的联邦学习算法进行了对比,实验结果表明FedDw算法具有更好的综合性能。
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