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Federated Learning (FL) enables model training across decentralized clients while preserving data privacy. However, bandwidth constraints limit the volume of information exchanged, making communication efficiency a critical challenge. In addition, non- IID data distributions require fairness-aware mechanisms to prevent performance degradation for certain clients. Existing sparsification techniques often apply fixed compression ratios uniformly, ignoring variations in client importance and bandwidth. We propose FedBand, a dynamic bandwidth allocation framework that prioritizes clients based on their contribution to the global model. Unlike conventional approaches, FedBand does not enforce uniform client participation in every communication round. Instead, it allocates more bandwidth to clients whose local updates deviate significantly from the global model, enabling them to transmit a greater number of parameters. Clients with less impactful updates contribute proportionally less or may defer transmission, reducing unnecessary overhead while maintaining generalizability. By optimizing the trade-off between communication efficiency and learning performance, FedBand substantially reduces transmission costs while preserving model accuracy. Experiments on non-IID CIFAR-10 and UTMobileNet2021 datasets, demonstrate that FedBand achieves up to 99.81% bandwidth savings per round while maintaining accuracies close to that of an unsparsified model (80% on CIFAR- 10, 95% on UTMobileNet), despite transmitting less than 1% of the model parameters in each round. Moreover, FedBand accelerates convergence by 37.4%, further improving learning efficiency under bandwidth constraints. Mininet emulations further show a 42.6% reduction in communication costs and a 65.57% acceleration in convergence compared to baseline methods, validating its real-world efficiency. These results demonstrate that adaptive bandwidth allocation can significantly enhance the scalability and communication efficiency of federated learning, making it more viable for real- world, bandwidth-constrained networking environments. 

A novel two-photon direct laser writing-based hybrid strategy for 3D nanoprinting microfluidic vessels with sophisticated 3D architectures and custom-designed micropores. 

Microinjection protocols that involve using a hollow, high-aspect-ratio microneedle to deliver foreign material (e.g., cells, DNA, viruses, and micro/nanoparticles) into biological targets (e.g., embryos, tissues, and organisms) are essential to diverse biomedical applications in both research and clinical settings. A key deficit of such protocols, however, is that standard microneedle architectures are inherently susceptible to clogging-induced failure modes, which can diminish experimental rigor and lead to failed microinjections. Additive manufacturing (or “three-dimensional (3D) printing”) strategies based on “Two-Photon Direct Laser Writing (DLW)” offer a promising route to address clogging failure phenomena by rearchitecting the needle tip, yet achieving 3D-printed microneedles with the mechanical strength necessary to penetrate into biological targets (e.g., embryos) has remained a critical barrier to efficacy. To overcome this barrier, here we harness a recently reported polyhedral oligomeric silsequioxane (POSS) photomaterial to DLW-print fused silica glass high-aspect-ratio microinjection needles with enhanced mechanical strength. Experimental results for POSS-based 3D-nanoprinted microneedles with inner and outer diameters of 10 μm and 15 μm, respectively, and heights ranging from 500–750 μm revealed that the needles not only enabled successful puncture and penetration into early-stage zebrafish embryos, but also significantly reduced the magnitude of undesired deformations to the embryos during needle puncture and penetration from 61.0±12.1 μm for standard glass-pulled control microneedles to 42.4±11.5 μm for the POSS-enabled 3D microneedles (p < 0.01). In combination, these results suggest that wide-ranging biomedical fields could benefit from the presented 3D microinjection needles.