Applying Federated Learning (FL) on Internet-of-Things devices is
necessitated by the large volumes of data they produce and growing concerns of
data privacy. However, there are three challenges that need to be addressed to
make FL efficient: (i) execution on devices with limited computational
capabilities, (ii) accounting for stragglers due to computational heterogeneity
of devices, and (iii) adaptation to the changing network bandwidths. This paper
presents FedAdapt, an adaptive offloading FL framework to mitigate the
aforementioned challenges. FedAdapt accelerates local training in
computationally constrained devices by leveraging layer offloading of deep
neural networks (DNNs) to servers. Further, FedAdapt adopts reinforcement
learning based optimization and clustering to adaptively identify which layers
of the DNN should be offloaded for each individual device on to a server to
tackle the challenges of computational heterogeneity and changing network
bandwidth. Experimental studies are carried out on a lab-based testbed and it
is demonstrated that by offloading a DNN from the device to the server FedAdapt
reduces the training time of a typical IoT device by over half compared to
classic FL. The training time of extreme stragglers and the overall training
time can be reduced by up to 57%. Furthermore, with changing network bandwidth,
FedAdapt is demonstrated to reduce the training time by up to 40% when compared
to classic FL, without sacrificing accuracy.

本篇文章提出了 FedAdapt，这是一种联邦学习框架，通过层次化的离线和强化学习优化，将深度神经网络中的特定层进行远程卸载，以解决物联网设备执行效率、设备计算异构性和网络带宽变化等方面的挑战，实验证明，相比传统的联邦学习，FedAdapt 可以将典型物联网设备的训练时间缩短一半以上，可以将极端滞后的训练时间和总体训练时间缩短高达 57％，在不损失准确性的情况下还可以将训练时间缩短多达 40％。