  Federated learning is used for decentralized training of machine learning
models on a large number (millions) of edge mobile devices. It is challenging
because mobile devices often have limited communication bandwidth and local
computation resources. Therefore, improving the efficiency of federated
learning is critical for scalability and usability. In this paper, we propose
to leverage partially trainable neural networks, which freeze a portion of the
model parameters during the entire training process, to reduce the
communication cost with little implications on model performance. Through
extensive experiments, we empirically show that Federated learning of Partially
Trainable neural networks (FedPT) can result in superior communication-accuracy
trade-offs, with up to $46\times$ reduction in communication cost, at a small
accuracy cost. Our approach also enables faster training, with a smaller memory
footprint, and better utility for strong differential privacy guarantees. The
proposed FedPT method can be particularly interesting for pushing the
limitations of over-parameterization in on-device learning.

Machine Learning