Enabling SQL-based training data debugging for federated learning

Liu, Yejia; Wu, Weiyuan; Flokas, Lampros; Wang, Jiannan; Wu, Eugene

doi:10.14778/3494124.3494125

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…Liu et al [67] proposed an SQL-based training data debugging framework for federated learning, which automatically removes label errors from training data to fix unexpected model behavior. The authors address technical challenges to make the framework secure, efficient, and accurate and propose Frog, a novel framework that outperforms their previous solution.…”

Section: Security and Privacy In Federated Learningmentioning

confidence: 99%

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Khan¹,

Hu²,

Sadoghi³

et al. 2023

Preprint

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Federated learning (FL) is a distributed machine learning approach that allows multiple clients to learn a shared model collaboratively without sharing their raw data. State-of-the-art FL systems provide an all-in-one solution; for example, users must install and use whichever data management subsystem that is expected by the FL system. This tightly-coupling paradigm is at best a hindrance to the wide adoption of FL solutions; in some domains, such as scientific applications, it could be a deal-breaker by enforcing a specific type of data solution on special hardware and platforms (e.g., high-performance computing clusters without node-local persistent storage). To this end, one natural solution is to decouple the data management functionalities from the FL system, enabling clients to customize their FL applications with specific data subsystems on which efficient queries can be made. The technical challenges of such decoupling methods include new system architectures, expressive and structured data models for FL parameters, and security guarantees among FL participants. As a starting point for this line of research, this paper conducts a thorough evaluation and comparison of mainstream database solutions as decoupled services in the context of FL systems. To make a fair comparison, we develop a framework called Data-Decoupling Federated Learning (DDFL), which can run FL workloads and is agnostic of the underlying database system by exposing a unified interface. To evaluate the effectiveness and feasibility of our approach, we compare DDFL with state-of-the-art FL systems that tightly couple data management and computational counterparts. We carry out extensive experiments on various datasets and data management subsystems (e.g., PostgreSQL, MongoDB, Cassandra, Neo4j) and show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time, while providing more options for clients to tune their FL applications regarding data-related metrics, such as performance, resilience, and usability. Additionally, we provide a detailed qualitative analysis of DDFL when being integrated with mainstream database systems.

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Section: Security and Privacy In Federated Learningmentioning

confidence: 99%

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Khan¹,

Hu²,

Sadoghi³

et al. 2023

Preprint

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A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

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FedST: secure federated shapelet transformation for time series classification

Liang,

Wang

2024

The VLDB Journal

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Enabling SQL-based training data debugging for federated learning

Cited by 8 publications

References 40 publications

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

FedST: secure federated shapelet transformation for time series classification

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