  The typical federated learning workflow requires communication between a
central server and a large set of clients synchronizing model parameters
between each other. The current frameworks use communication protocols not
suitable for resource-constrained devices and are either hard to deploy or
require high-throughput links not available on these devices. In this paper, we
present a generic message framework using CBOR for communication with existing
federated learning frameworks optimised for use with resource-constrained
devices and low power and lossy network links. We evaluate the resulting
message sizes against JSON serialized messages where compare both with model
parameters resulting in optimal and worst case serialization length, and with a
real-world LeNet-5 model. Our benchmarks show that with our approach, messages
are up to 75 % smaller in size when compared to the JSON alternative.
