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Title: Edge-assisted Democratized Learning Towards Federated Analytics
Other Titles: Choong Seon Hong*
Authors: Shashi, Raj Pandey
Nguyen, Huu Nhat Minh
Nguyen, Dang Tri
Kyi, Thar
Nguyen, H. Tran
Han, Zhu
Hong, Choong Seon
Keywords: Computational modeling
Distributed databases
Computer architecture
Data models
Performance evaluation
Analytical models
Issue Date: Jan-2022
Publisher: IEEE
Abstract: A recent take toward federated analytics (FA), which allows analytical insights of distributed data sets, reuses the federated learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that 1) unleashes a viable infrastructure for FA and 2) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. First, we show the hierarchical learning structure of the proposed edge-assisted Dem-AI mechanism, namely Edge-DemLearn , as a practical framework to empower generalization capability in support of FA. Second, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application’s scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real data sets demonstrate the effectiveness of the proposed methods.
Description: IEEE Internet of Things Journal (Volume: 9, Issue: 1)
ISSN: 2327-4662
Appears in Collections:NĂM 2022

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