  Federated learning (FL) is a general principle for decentralized clients to
train a server model collectively without sharing local data. FL is a promising
framework with practical applications, but its standard training paradigm
requires the clients to backpropagate through the model to compute gradients.
Since these clients are typically edge devices and not fully trusted, executing
backpropagation on them incurs computational and storage overhead as well as
white-box vulnerability. In light of this, we develop backpropagation-free
federated learning, dubbed BAFFLE, in which backpropagation is replaced by
multiple forward processes to estimate gradients. BAFFLE is 1) memory-efficient
and easily fits uploading bandwidth; 2) compatible with inference-only hardware
optimization and model quantization or pruning; and 3) well-suited to trusted
execution environments, because the clients in BAFFLE only execute forward
propagation and return a set of scalars to the server. Empirically we use
BAFFLE to train deep models from scratch or to finetune pretrained models,
achieving acceptable results. Code is available in
https://github.com/FengHZ/BAFFLE.

Machine Learning;Artificial Intelligence;Cryptography and Security;Distributed, Parallel, and Cluster Computing