  Federated learning (FL) is usually performed on resource-constrained edge
devices, e.g., with limited memory for the computation. If the required memory
to train a model exceeds this limit, the device will be excluded from the
training. This can lead to a lower accuracy as valuable data and computation
resources are excluded from training, also causing bias and unfairness. The FL
training process should be adjusted to such constraints. The state-of-the-art
techniques propose training subsets of the FL model at constrained devices,
reducing their resource requirements for training. But these techniques largely
limit the co-adaptation among parameters of the model and are highly
inefficient, as we show: it is actually better to train a smaller (less
accurate) model by the system where all the devices can train the model
end-to-end, than applying such techniques. We propose a new method that enables
successive freezing and training of the parameters of the FL model at devices,
reducing the training's resource requirements at the devices, while still
allowing enough co-adaptation between parameters. We show through extensive
experimental evaluation that our technique greatly improves the accuracy of the
trained model (by 52.4 p.p.) compared with the state of the art, efficiently
aggregating the computation capacity available on distributed devices.
