Federated Active Semi-Supervised Learning With Communication Efficiency

Zhang, Chen; Xie, Yu; Bai, Hang; Hu, Xiongwei; Yu, Bin; Gao, Yimin

doi:10.1109/tsmc.2023.3286485

Cited by 1 publication

(1 citation statement)

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“…First, this work builds latent vectors that reflect each client’s data distribution to accomplish this. The settings of the UMHFLM modules are then customized for each client by conditioning the resultant embeddings on the hypernetworks ( Zhang et al, 2023 ). This article successfully factorizes the hypernetworks’ weights, considering the numerous parameters that are derived from them.…”

Section: Proposed Methodologymentioning

confidence: 99%

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

2024

PeerJ Computer Science

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More sophisticated data access is possible with artificial intelligence (AI) techniques such as question answering (QA), but regulations and privacy concerns have limited their use. Federated learning (FL) deals with these problems, and QA is a viable substitute for AI. The utilization of hierarchical FL systems is examined in this research, along with an ideal method for developing client-specific adapters. The User Modified Hierarchical Federated Learning Model (UMHFLM) selects local models for users’ tasks. The article suggests employing recurrent neural network (RNN) as a neural network (NN) technique for learning automatically and categorizing questions based on natural language into the appropriate templates. Together, local and global models are developed, with the worldwide model influencing local models, which are, in turn, combined for personalization. The method is applied in natural language processing pipelines for phrase matching employing template exact match, segmentation, and answer type detection. The (SQuAD-2.0), a DL-based QA method for acquiring knowledge of complicated SPARQL test questions and their accompanying SPARQL queries across the DBpedia dataset, was used to train and assess the model. The SQuAD2.0 datasets evaluate the model, which identifies 38 distinct templates. Considering the top two most likely templates, the RNN model achieves template classification accuracy of 92.8% and 61.8% on the SQuAD2.0 and QALD-7 datasets. A study on data scarcity among participants found that FL Match outperformed BERT significantly. A MAP margin of 2.60% exists between BERT and FL Match at a 100% data ratio and an MRR margin of 7.23% at a 20% data ratio.

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Section: Proposed Methodologymentioning

confidence: 99%

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

Federated Active Semi-Supervised Learning With Communication Efficiency

Cited by 1 publication

References 30 publications

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

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