FedAdapter: Efficient Federated Learning for Modern NLP

TL;DR

FedAdapter introduces an adaptive framework that significantly accelerates federated NLP training by dynamically configuring adapter modules, enabling practical deployment of large pre-trained models across diverse tasks and resource constraints.

Contribution

The paper proposes FedAdapter, a novel method that automates adapter configuration and progressively upgrades them during training to improve efficiency in federated NLP.

Findings

Reduces FedNLP convergence time by up to 155.5x

Enables training of large models on mobile devices

Achieves faster training without sacrificing accuracy

Abstract

Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, our measurements show that FedNLP is prohibitively slow due to the large model sizes and the resultant high network/computation cost. Towards practical FedNLP, we identify as the key building blocks adapters, small bottleneck modules inserted at a variety of model layers. A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive. No silver-bullet configuration exists: the optimal choice varies across downstream NLP tasks, desired model accuracy, and mobile resources. To automate adapter configuration, we propose FedAdapter, a framework that enhances the existing FedNLP with two novel designs. First, FedAdapter progressively upgrades the adapter configuration throughout a training session; the principle is to quickly learn shallow knowledge by only training fewer and smaller adapters at the model's top layers, and incrementally learn deep knowledge by incorporating deeper and larger adapters. Second, FedAdapter continuously profiles future adapter configurations by allocating participant devices to trial groups. Extensive experiments show that FedAdapter can reduce FedNLP's model convergence delay to no more than several hours, which is up to 155.5$\times$ faster compared to vanilla FedNLP and 48$\times$ faster compared to strong baselines.