Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO.Comment: 18 pages, accepted by EMNLP 202

Optimal Look-ahead Control of CSPS System by Deep Q-Network and Profit Sharing

International audienceThe look-ahead control problem in the conveyor-serviced production station (CSPS) system is an important topic of research in an intelligent production line. Some studies has applied various kinds of intelligent algorithms to reduce the average discount cost of the system. There are three goals in this paper. We proposed the algorithm based on Deep Q-network (DQN), applied to the CSPS system to achieve an effective reduction of the average cost. The oscillations in the learning process is reduced through the profit sharing (PS) algorithm. Moreover, we not only proposed the algorithm for CSPS system with DQN, but also further optimized the intelligent look-ahead control of CSPS system by combining it with PS, thereby enabling the processing rate of each production station in the CSPS system to achieve optimal. The learning efficiency, convergence result and stability during the learning process were used as the main evaluation criteria to analyze the experimental results. The results show that the combination of DQN and PS can effectively optimize the performance of look-ahead control when the parameters are selected reasonably

The Inhibitory Effect of Quercetin on Asymmetric Dimethylarginine-Induced Apoptosis Is Mediated by the Endoplasmic Reticulum Stress Pathway in Glomerular Endothelial Cells

Asymmetric dimethylarginine (ADMA) is considered an independent mortality and cardiovascular risk factor in chronic kidney disease (CKD) patients, and contributes to the development of renal fibrosis. Quercetin (QC), a natural component of foods, protects against renal injury. Here, we explored the possible mechanisms that are responsible for ADMA-induced renal fibrosis and the protective effect of QC. We found that ADMA treatment activated the endoplasmic reticulum (ER) stress sensor proteins phosphorylated protein kinase RNA-activated-like ER kinase (PERK) and inositol requiring-1α (IRE1), which correspondingly induced C/EBP homologous protein (CHOP) expression and phosphorylated c-Jun N-terminal kinase (JNK) phosphorylation in glomerular endothelial cells (GEnCs). Following this, ADMA promoted ER stress-induced apoptosis and resulted in transforming growth factor β (TGF-β) expression in GEnCs. SP600125, an inhibitor of JNK, and CHOP siRNA protected against ADMA-induced cell apoptosis and TGF-β expression. QC prevented ADMA-induced PERK and IRE1 apoptotic ER stress pathway activation. Also, ADMA-induced GEnCs apoptosis and TGF-β expression was reduced by QC. Overexpression of CHOP blocked QC-mediated protection from apoptosis in ER stressed cells. Overall, these observations indicate that ADMA may induce GEnCs apoptosis and TGF-β expression by targeting the PERK-CHOP and IRE1-JNK pathway. In addition, drugs such as QC targeting ER stress may hold great promise for the development of novel therapies against ADMA-induced renal fibrosis