  The advent of large language models (LLMs) has revolutionized the deep
learning paradigm, yielding impressive results across a wide array of tasks.
However, the pre-training or fine-tuning of LLMs within a federated learning
(FL) framework poses substantial challenges, including considerable
computational and memory resource demands, as well as communication bottlenecks
between servers and clients. Existing solutions either make the unrealistic
assumption that the entire model is exchanged for training, or apply
parameter-effective fine-tuning methods from centralized learning to train LLMs
in FL which tend to underperform during training or fine-tuning stages due to
the limited search subspace of parameter updating. In this paper, we introduce
a novel method for the efficient training and fine-tuning of LLMs in FL, with
minimal resource consumption. Our approach, termed FedCyBGD, utilizes Cycle
Block Gradient Descent to periodically update the model. In particular, we
design a compression scheme for FedCyBGD, aiming to further decrease the model
download cost. It enables full parameter training in FL with only selected
block updates and uploads, thereby reducing communication, computation, and
memory costs. Our method achieves state-of-the-art performance for FL LLM
training, while significantly reducing associated costs. Codes are provided
here.
