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

arXiv 2303.08371 ↗First posted 15 Mar 2023cs.DB cs.AI cs.DCcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a machine learning approach that allows multiple clients to collaboratively learn a shared model without sharing raw data. However, current FL systems provide an all-in-one solution, which can hinder the wide adoption of FL in certain domains such as scientific applications. To overcome this limitation, this paper proposes a decoupling approach that enables clients to customize FL applications with specific data subsystems. To evaluate this approach, the authors develop a framework called Data-Decoupling Federated Learning (DDFL) and compare it with state-of-the-art FL systems that tightly couple data management and computation. Extensive experiments on various datasets and data management subsystems show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time. Moreover, DDFL provides clients with more options to tune their FL applications regarding data-related metrics. The authors also provide a detailed qualitative analysis of DDFL when integrated with mainstream database systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 66 references that are in the map. First posted on arXiv 15 Mar 2023.

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References · 30

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18 more
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