FedFwd: Federated Learning without Backpropagation
Seonghwan Park Dahun Shin Jinseok Chung Namhoon Lee
In federated learning (FL), clients with limited resources can disrupt the
training efficiency. A potential solution to this problem is to leverage a new
learning procedure that does not rely on backpropagation (BP). We present a
novel approach to FL called FedFwd that employs a recent BP-free method by
Hinton (2022), namely the Forward Forward algorithm, in the local training
process. FedFwd can reduce a significant amount of computations for updating
parameters by performing layer-wise local updates, and therefore, there is no
need to store all intermediate activation values during training. We conduct
various experiments to evaluate FedFwd on standard datasets including MNIST and
CIFAR-10, and show that it works competitively to other BP-dependent FL
methods.