  A fundamental issue for federated learning (FL) is how to achieve optimal
model performance under highly dynamic communication environments. This issue
can be alleviated by the fact that modern edge devices usually can connect to
the edge FL server via multiple communication channels (e.g., 4G, LTE and 5G).
However, having an edge device send copies of local models to the FL server
along multiple channels is redundant, time-consuming, and would waste resources
(e.g., bandwidth, battery life and monetary cost). In this paper, motivated by
the layered coding techniques in video streaming, we propose a novel FL
framework called layered gradient compression (LGC). Specifically, in LGC,
local gradients from a device is coded into several layers and each layer is
sent to the FL server along a different channel. The FL server aggregates the
received layers of local gradients from devices to update the global model, and
sends the result back to the devices. We prove the convergence of LGC, and
formally define the problem of resource-efficient federated learning with LGC.
We then propose a learning based algorithm for each device to dynamically
adjust its local computation (i.e., the number of local stochastic descent) and
communication decisions (i.e.,the compression level of different layers and the
layer to channel mapping) in each iteration. Results from extensive experiments
show that using our algorithm, LGC significantly reduces the training time,
improves the resource utilization, while achieving a similar accuracy, compared
with well-known FL mechanisms.
