  Federated learning (FL) enables multiple clients to collaboratively train a
shared model without disclosing their local datasets. This is achieved by
exchanging local model updates with the help of a parameter server (PS).
However, due to the increasing size of the trained models, the communication
load due to the iterative exchanges between the clients and the PS often
becomes a bottleneck in the performance. Sparse communication is often employed
to reduce the communication load, where only a small subset of the model
updates are communicated from the clients to the PS. In this paper, we
introduce a novel time-correlated sparsification (TCS) scheme, which builds
upon the notion that sparse communication framework can be considered as
identifying the most significant elements of the underlying model. Hence, TCS
seeks a certain correlation between the sparse representations used at
consecutive iterations in FL, so that the overhead due to encoding and
transmission of the sparse representation can be significantly reduced without
compromising the test accuracy. Through extensive simulations on the CIFAR-10
dataset, we show that TCS can achieve centralized training accuracy with 100
times sparsification, and up to 2000 times reduction in the communication load
when employed together with quantization.
