Evidence map›Paper›PMID 40642521›Full record

ArticleConference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security2024

Cross-silo Federated Learning with Record-level Personalized Differential Privacy.

Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng

Abstract read
In one paragraph

Article in Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security, 2024. 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.

  1. Enhanced Privacy Bound for Shuffle Model with Personalized Privacy.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024
    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.

Junxu LiuRenmin University of China, Beijing, China.
Jian LouZhejiang University, Hangzhou, China.
Li XiongEmory University, Atlanta, USA.
Jinfei LiuZhejiang University, Hangzhou, China.
Xiaofeng MengRenmin University of China, Beijing, China.

Funding

Decentralized differentially-private methods for dynamic data release and analysisR01LM013712 · NLM · YALE UNIVERSITY · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2022 to 2025
$2.5M
Sensor Hardware and Intelligent Tools for Assessing the Health Effects of Heat ExposureR01ES033241 · NIEHS · EMORY UNIVERSITY · PI HERTZBERG, VICKI STOVER, XIONG, LI · 2022 to 2025
$2.5M
NIEHS NIH HHS R01 ES033241NLM NIH HHS R01 LM013712
6 · The paper itself

Abstract

Federated learning (FL) enhanced by differential privacy has emerged as a popular approach to better safeguard the privacy of client-side data by protecting clients' contributions during the training process. Existing solutions typically assume a uniform privacy budget for all records and provide one-size-fits-all solutions that may not be adequate to meet each record's privacy requirement. In this paper, we explore the uncharted territory of cross-silo FL with record-level personalized differential privacy. We devise a novel framework named

Indexed as

Differential PrivacyFederated LearningPersonalized Privacy Protection

Identifiers

PMID40642521
PMCPMC12241667

What OpenQuestion holds

Textmetadata
LicenceTDM
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.