Reduce Communication Costs and Preserve Privacy: Prompt Tuning Method in Federated Learning. (arXiv:2208.12268v1 [cs.LG])

Federated learning (FL) has enabled global model training on decentralized
data in a privacy-preserving way by aggregating model updates. However, for
many natural language processing (NLP) tasks that utilize pre-trained language
models (PLMs) with large numbers of parameters, there are considerable
communication costs associated with FL. Recently, prompt tuning, which tunes
some soft prompts without modifying PLMs, has achieved excellent performance as
a new learning paradigm. Therefore we want to combine the two methods and
explore the effect of prompt …

communication federated learning lg privacy