Dual prompt personalized federated learning in foundation models.

Ying Chang, Xiaohu Shi, Xiaohui Zhao, Zhaohuang Chen, Deyin Ma

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

FedSCOPE: Federated cross-domain sequential recommendation with decoupled contrastive learning and privacy-preserving semantic enhancement.Scientific reports · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ying ChangCollege of Software, Jilin University, Changchun, 130012, China.

Xiaohu ShiCollege of Software, Jilin University, Changchun, 130012, China. shixh@jlu.edu.cn.

Xiaohui ZhaoCollege of Software, Jilin University, Changchun, 130012, China.

Zhaohuang ChenCollege of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Deyin MaCollege of Computer Science and Engineering, Changchun University of Technology, Changchun, 130000, China. madeyin@ccut.edu.cn.

Funding

Science-Technology Development Plan Project of Jilin Province 20210202129NC

6 · The paper itself

Abstract

Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insufficient training, leading to suboptimal performance. Foundation models, such as CLIP (Contrastive Language-Image Pretraining), exhibit strong feature extraction capabilities and can alleviate this issue by fine-tuning on limited local data. Despite their potential, foundation models are rarely utilized in federated learning scenarios, and challenges related to integrating new clients remain largely unresolved. To address these challenges, we propose the Dual Prompt Personalized Federated Learning (DP

Indexed as

Federated LearningDeep LearningHumansMachine LearningModels, TheoreticalAdaptive aggregation strategyClient heterogeneityFoundation modelsPersonalized federated learning

Identifiers

PMID40745444

DOI10.1038/s41598-025-11864-4

PMCPMC12313890

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.