While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Add...
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Evaluating the factors affecting customer value in department stores will shed light on the motivations of customers when choosing department stores, which will help department stores to improve their business perform...
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Anonymous property is a fundamental right for legitimate customers when he/she was shopping. But it could be misused by criminals. In this paper, we will propose an efficient and new blind signature (BS). The proposed...
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Given that reefer containers are highly dependent on their temperature control function, container carriers must pay particular attention to the risks occurring during cold chain logistics. Assessment of cold chain sa...
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programming ability is the core ability of this era and can be obtained and improved through practice. In this paper, an Automatedprogramming Assessment system based on Mastery learning and Peer competition (APAMP) w...
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This study proposed a novel extraction method of c-Fos protein regions in dAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convo...
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An audio watermarking technique using Complementary Ensemble Empirical Mode decomposition and group differential relations of average absolute amplitudes of the last Intrinsic Mode Function (IMF) is proposed. By using...
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Indoor understanding is currently a topic that is widely studied in the field of machine learning. Furniture is the most common object in indoor scenes, just as various vehicles are most commonly seen in street scenes...
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Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client pa...
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Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client participation) due to various system heterogeneity factors. A popular solution is the server-assisted federated learning (SA-FL) framework, where the server uses an auxiliary dataset. despite empirical evidence of SA-FL's effectiveness in addressing incomplete client participation, theoretical understanding of SA-FL is lacking. Furthermore, the effects of incomplete client participation in conventional FL are poorly understood. This motivates us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is not PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under incomplete client participation, we propose the SAFARI (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show SAFARI significantly improves the performance under incomplete client participation. Copyright 2024 by the author(s)
Self-explanatory artificial intelligence has lately gained prominence as researchers and practitioners strive to make their methodologies more understandable. It is not contentious to assert that researching how indiv...
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