Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves cluste...
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Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.
In 2019, SATURNIN a light-weighted block cipher was proposed for post-quantum security. In this research, we try to examine the security strength of SATURNIN by implementing its quantum circuit to apply Grover’s algo...
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Hierarchical text classification is a challenging task, in particular when complex taxonomies, characterized by multi-level labeling structures, need to be handled. A critical aspect of the task lies in the scarcity o...
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We introduce a new observational setting for Positive Unlabeled (PU) data where the observations at prediction time are also labeled. This occurs commonly in practice - we argue that the additional information is impo...
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作者：
Biersack, FlorianInstitute of Mathematics
Faculty of Mathematics and Computer Science Julius–Maximilians–Universität Würzburg Emil–Fischer–Strasse 40 Bavaria Würzburg 97074 Germany
We study the quasiconformal automorphism groups Q(G) of bounded, simply connected domains G ⊊ℂ in the complex plane, endowed with the topology of uniform convergence. By taking into consideration their canonical subsp...
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Recent advances in applying Large Language Models (LLMs) to natural language processing raise the challenge of integrating them with ontological models, to harness the features of Knowledge Graphs (KG) alongside the e...
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Large Language Models (LLMs) have become the hot topic in Artificial Intelligence (AI) in the last few years, especially with the advent of the Generative Pretrained Transformer (GPT) models and the release of ChatGPT...
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Personalized learning plays a critical role in enhancing educational outcomes by customizing experiences to meet individual learners' unique needs, preferences, and abilities. Although Large Language Models (LLMs)...
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In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute ...
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In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute value equation(AVE). The proposed method not only includes SOR-like method, FPI method, MFPI method and so on, but also generates some special versions. Some convergence conditions of the proposed method with different iteration error norms are presented. Furthermore, methods corresponding to other splitting methods are studied in detail. The effectiveness and feasibility of the proposed method are confirmed by some numerical experiments.
Taking care of the protection of encrypted data against the abilities of quantum computers to decode hidden data, encryption methods are currently being intensively researched and quantum-resistant e-infrastructure tr...
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