Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have explored discrete representations to enhance model generalization across minor distributional shifts, these approaches often struggle to adapt to new data silos with significantly divergent distributions. In response, we have identified that models derived from FL exhibit markedly increased uncertainty when applied to data silos with unfamiliar distributions. Consequently, we propose an innovative yet straightforward iterative framework, termed Uncertainty-Based Extensible-Codebook Federated Learning (UEFL). This framework dynamically maps latent features to trainable discrete vectors, assesses the uncertainty, and specifically extends the discretization dictionary or codebook for silos exhibiting high uncertainty. Our approach aims to simultaneously enhance accuracy and reduce uncertainty by explicitly addressing the diversity of data distributions, all while maintaining minimal computational overhead in environments characterized by heterogeneous data silos. Through experiments conducted on five datasets, our method has demonstrated its superiority, achieving significant improvements in accuracy (by 3%--22.1%) and uncertainty reduction (by 38.83%--96.24%), thereby outperforming contemporary state-of-the-art methods. The source code is available at https://github.com/destiny301/uefl

A Survey on Effective Invocation Methods of Massive LLM Services

Language models as a service (LMaaS) enable users to accomplish tasks without requiring specialized knowledge, simply by paying a service provider. However, numerous providers offer massive large language model (LLM) services with variations in latency, performance, and pricing. Consequently, constructing the cost-saving LLM services invocation strategy with low-latency and high-performance responses that meet specific task demands becomes a pressing challenge. This paper provides a comprehensive overview of the LLM services invocation methods. Technically, we give a formal definition of the problem of constructing effective invocation strategy in LMaaS and present the LLM services invocation framework. The framework classifies existing methods into four different components, including input abstract, semantic cache, solution design, and output enhancement, which can be freely combined with each other. Finally, we emphasize the open challenges that have not yet been well addressed in this task and shed light on future research

Fabrication of hydrophobic inorganic coatings on natural lotus leaves for nanoimprint stamps

Hydrophobic inorganic films were obtained by direct deposition of copper or silicon onto natural lotus leaves by ion beam sputtering deposition technique. Scanning electron microscopy observations showed a lotus-leaf-like surface structure of the deposited inorganic films. Hydrophobic nature of the inorganic films on lotus leaves had been improved compared to the inorganic films deposited on flat silicon substrates. Water contact angles measured on the lotus-leaf-like copper and silicon films were 136.3 \pm 8{\deg} and 117.8 \pm 4.4{\deg}, respectively. The hydrophobic lotus-leaf-like inorganic films had been repeated used as nanoimprint stamps. Negative structures of lotus-leaf-like inorganic films were obtained on the polystyrene resist layers.Comment: 14 pages, 6 figure