  In this paper, we propose Helios, a heterogeneity-aware FL framework to
tackle the straggler issue. Helios identifies individual devices' heterogeneous
training capability, and therefore the expected neural network model training
volumes regarding the collaborative training pace. For straggling devices, a
"soft-training" method is proposed to dynamically compress the original
identical training model into the expected volume through a rotating neuron
training approach. With extensive algorithm analysis and optimization schemes,
the stragglers can be accelerated while retaining the convergence for local
training as well as federated collaboration.
