Scaling Language Model Size in Cross-Device Federated Learning

Abstract

Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applications of partial model training, quantization, efficient transfer learning, and communication-efficient optimizers, we are able to train a 21M parameter Transformer that achieves the same perplexity as that of a similarly sized LSTM with ∼ 10× smaller client-to-server communication cost and 11% lower perplexity than smaller LSTMs commonly studied in literature.