Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models

Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal large models trained on public datasets often underperform in specific industrial domains. This paper proposes a multimodal federated learning framework that enables multiple enterprises to utilize private domain data to collaboratively train large models for vertical domains, achieving intelligent services across scenarios. The authors discuss in-depth the strategic transformation of federated learning in terms of intelligence foundation and objectives in the era of big model, as well as the new challenges faced in heterogeneous data, model aggregation, performance and cost trade-off, data privacy, and incentive mechanism. The paper elaborates a case study of leading enterprises contributing multimodal data and expert knowledge to city safety operation management , including distributed deployment and efficient coordination of the federated learning platform, technical innovations on data quality improvement based on large model capabilities and efficient joint fine-tuning approaches. Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management. The established federated learning cooperation ecosystem is expected to further aggregate industry, academia, and research resources, realize large models in multiple vertical domains, and promote the large-scale industrial application of artificial intelligence and cutting-edge research on multimodal federated learning

Comparative study of eggshell antibacterial effectivity in precocial and altricial birds using Escherichia coli.

In this study, we compared the antibacterial effectivity of the eggs of six precocial and four altricial bird species using Escherichia coli, based on their eggshell traits. The ultrastructure of eggshell was observed using a scanning electron microscope (SEM). According to SEM results, eggs from precocial birds (chicken, turkey, quail, duck, ostrich, and goose) had cuticle on the eggshells, while eggs from altricial birds (pigeon, budgerigar, munia, and canary) did not. The environment/selection pressure may induce the divergent evolution process in eggs of precocial and altricial birds. The E. coli experiment results showed that chicken, turkey, quail, duck, and goose eggs, with a high cuticle opacity, exhibited a much lower E. coli penetration rate. In contrast, the eggs with poor (ostrich) or without (pigeon, budgerigar, munia, and canary) cuticle exhibited a higher penetration rate. It is suggested that cuticle is a main barrier against bacterial penetration in precocial birds' eggs. Turkey and quail eggs showed the lowest E. coli contamination rate (3.33% and 2.22%, respectively), probably because of the tightly connected nanosphere structure on their cuticle. As for altricial birds' eggs, the eggs of budgerigar, munia, and canary with small pore diameter (0.57 to 1.22 μm) had a lower E. coli penetration rate than pigeon eggs (45.56%, 66.67%, 50%, and 97.78%, respectively, P < 0.05), indicating that pore diameter played a significant role in defending against bacterial trans-shell invasion. We found that eggshell thickness and pore area decreased with egg size. The cuticle quality had no relationship with egg size, but was closely related to the bird species. The E. coli penetration rate of altricial birds' eggs was significantly higher than that of precocial birds' eggs, mainly because the pores are exposed on the eggshell surface and cuticle protection is absent. This study provides detailed information on the eggshell cuticle, which gives insight into the cuticle evolution process that occurred in precocial and altricial bird species. Moreover, the results of E. coli penetration may help understanding the antibacterial behavior in birds

Highly Efficient Phosphate Sequestration in Aqueous Solutions Using Nanomagnesium Hydroxide Modified Polystyrene Materials

Phosphate removal is important for the control of eutrophication, and adsorption may serve as a powerful supplement to biological phosphate sequestration. Here, we develop a new composite adsorbent (denoted as HMO-PN) by encapsulating active nano-Mg­(OH)₂ onto macroporous polystyrene beads modified with fixed quaternary ammonium groups [CH₂N⁺(CH₂)₃Cl]. The N⁺-tailored groups can accelerate the diffusion of target phosphate through electrostatic attractions. The performance of the as-prepared HMO-PN was found to depend on the pH value of an aqueous medium. HMO-PN also exhibits high sorption selectivity toward the target phosphate. Kinetic equilibrium of phosphate adsorption can be achieved within 100 min, and the calculated maximum adsorption capacity is approximately 1.47 mmol/g (45.6 mg/g). Column experiments further show that the effluent concentration of phosphate can be reduced to below 0.5 mg/L (500 BV), suggesting highly efficient phosphate sequestration. Moreover, the exhausted HMO-PN can be readily regenerated using an alkaline brine solution