  In Federated Learning (FL), clients may have weak devices that cannot train
the full model or even hold it in their memory space. To implement large-scale
FL applications, thus, it is crucial to develop a distributed learning method
that enables the participation of such weak clients. We propose EmbracingFL, a
general FL framework that allows all available clients to join the distributed
training regardless of their system resource capacity. The framework is built
upon a novel form of partial model training method in which each client trains
as many consecutive output-side layers as its system resources allow. Our study
demonstrates that EmbracingFL encourages each layer to have similar data
representations across clients, improving FL efficiency. The proposed partial
model training method guarantees convergence to a neighbor of stationary points
for non-convex and smooth problems. We evaluate the efficacy of EmbracingFL
under a variety of settings with a mixed number of strong, moderate (~40%
memory), and weak (~15% memory) clients, datasets (CIFAR-10, FEMNIST, and
IMDB), and models (ResNet20, CNN, and LSTM). Our empirical study shows that
EmbracingFL consistently achieves high accuracy as like all clients are strong,
outperforming the state-of-the-art width reduction methods (i.e. HeteroFL and
FjORD).
