FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

arXiv:2404.18848v1 Announce Type: new
Abstract: Pre-trained Language Models (PLMs) have shown excellent performance on various downstream tasks after fine-tuning. Nevertheless, the escalating concerns surrounding user privacy have posed significant challenges to centralized training reliant on extensive data collection. Federated learning(FL), which only requires training on the clients and aggregates weights on the server without sharing data, has emerged as a solution. However, the substantial parameter size of PLMs places a significant burden on the computational resources of client devices, while …

abstract arxiv challenges collection concerns cs.ai cs.lg data data collection federated learning fine-tuning language language models performance privacy tasks training type