  While data is distributed in multiple edge devices, Federated Learning (FL)
is attracting more and more attention to collaboratively train a machine
learning model without transferring raw data. FL generally exploits a parameter
server and a large number of edge devices during the whole process of the model
training, while several devices are selected in each round. However, straggler
devices may slow down the training process or even make the system crash during
training. Meanwhile, other idle edge devices remain unused. As the bandwidth
between the devices and the server is relatively low, the communication of
intermediate data becomes a bottleneck. In this paper, we propose
Time-Efficient Asynchronous federated learning with Sparsification and
Quantization, i.e., TEASQ-Fed. TEASQ-Fed can fully exploit edge devices to
asynchronously participate in the training process by actively applying for
tasks. We utilize control parameters to choose an appropriate number of
parallel edge devices, which simultaneously execute the training tasks. In
addition, we introduce a caching mechanism and weighted averaging with respect
to model staleness to further improve the accuracy. Furthermore, we propose a
sparsification and quantitation approach to compress the intermediate data to
accelerate the training. The experimental results reveal that TEASQ-Fed
improves the accuracy (up to 16.67% higher) while accelerating the convergence
of model training (up to twice faster).
