In this paper, GeoFL develops a hierarchical federated learning (FL) framework to address the unique challenges in large-scale geo-distributed scenarios. The key idea is to deploy multiple aggregators to geo-distributed clients and aggregate the local model and the global model efficiently and effectively. By assigning each aggregator as a relay layer, GeoFL can elaborately aggregate the geo-distributed clients and systematically determine when to upload the model to the central server based on bandwidth to efficiently update the global model under inadequate and heterogeneous WAN bandwidth constraints. GeoFL designs three key components to optimize the inefficient model aggregation and cope with the non-importance model updates. It further addresses the statistical heterogeneity across geo-distributed aggregators by considering the clients’ graph relationship, delivering an end-to-end clien-taggregator- server architecture for large-scale clients. Compared with existing works, our results on large-scale real-life datasets show that GeoFL speeds up the training process by 1.4×–8× and reduces 6%–80% unnecessary communication rounds between the aggregator and the central server.

In this paper, we propose LoRaSeek, a lightweight and reliable LoRa denoising framework that enhances signal quality and robustness for neural-enhanced LoRa decoding. LoRaSeek integrates a hybrid architecture combining Convolutional Neural Networks (CNNs), Transformers, and a hierarchical U-Net to effectively capture multi-scale, multidimensional features of LoRa chirp signals. To maintain efficiency, we integrate a lightweight Transformer block that supports various LoRa configurations while keeping computational overhead low. Additionally, we incorporate dual attention-based skip connections to preserve chirp signal properties across different scales. Experiments across diverse LoRa configurations show that LoRaSeek achieves 2.04–3.86 dB signal-to-noise ratio (SNR) gains over standard decoding methods and up to 3.03 dB improvement over state-of-the-art neural-enhanced LoRa decoding methods while reducing model storage by up to 7.4× and inference time by up to 1.6×.

Accurate leaf wetness detection is essential to understanding plant health and growth conditions. The mmWave radar, with its sensitivity to subtle changes, is well-suited for leaf wetness detection. Existing mmWave-based approaches utilize the Synthetic Aperture Radar (SAR) algorithm to generate image-like inputs and rely on multi-modality fusion with an RGB camera to classify leaf wetness. However, the lack of understanding of SAR-based mmWave imaging limits its accuracy in various environments. This paper presents Proteus, a novel way of understanding mmWave SAR imaging. We design a noise reduction algorithm to reduce speckle noise and improve image clarity for SAR-based mmWave imaging. Then, we incorporate phase angle data to enrich SAR texture information to capture high-resolution surface details, increasing informative features for precise wetness assessment in complex plant structures. Additionally, we introduce a cross-modality Teacher-Student network, using an RGB-based teacher model to guide the mmWave SAR-based student model for feature extraction. This network transfers the explicit knowledge in the RGB image domain to the mmWave image domain. We use commercial-off-the-shelf mmWave radar to prototype Proteus. The evaluation results show that Proteus achieves up to 96.3% accuracy across varied environmental scenarios, outperforming state-of-the-art methods.

Leaf Wetness Duration (LWD), the time that water remains on leaf surfaces, is crucial in the development of plant diseases. Existing LWD detection lacks standardized measurement techniques, and variations across different plant characteristics limit its effectiveness. Prior research proposes diverse approaches, but they fail to measure real natural leaves directly and lack resilience in various environmental conditions. This reduces the precision and robustness, revealing a notable practical application and effectiveness gap in real-world agricultural settings. This paper presents Hydra, an innovative approach that integrates millimeter-wave (mm-Wave) radar with camera technology to detect leaf wetness by determining if there is water on the leaf. We can measure the time to determine the LWD based on this detection. Firstly, we design a Convolutional Neural Network (CNN) to selectively fuse multiple mm-Wave depth images with an RGB image to generate multiple feature images. Then, we develop a transformer-based encoder to capture the inherent connection among the multiple feature images to generate a feature map, which is further fed to a classifier for detection. Moreover, we augment the dataset during training to generalize our model. Implemented using a frequency-modulated continuous-wave (FMCW) radar within the 76 to 81 GHz band, Hydra's performance is meticulously evaluated on plants, demonstrating the potential to classify leaf wetness with up to 96% accuracy across varying scenarios. Deploying Hydra in the farm, including rainy, dawn, or poorly light nights, it still achieves an accuracy rate of around 90%.

Leaf wetness detection is one of the key technologies for preventing plant diseases in agriculture. In this poster, we propose mmLeaf, leveraging a commercial off-the-shelf millimeter-wave (mmWave) radar to detect actual leaf wetness in diverse environments and lighting conditions. mmLeaf captures mmWave signals reflected by monitored leaves with a two-dimensional (2D) scanning system. Then, we use a multiple-input multiple-output (MIMO) array and synthetic aperture radar (SAR) to reconstruct the signal distribution of different planes of the leaves. A deep learning model takes the fused signal distribution as inputs to classify the leaf wetness. We implement mmLeaf using a frequency-modulated continuous-wave (FMCW) radar and evaluate its performance with a potted plant indoors. By exploring the use of mmWave signals, mmLeaf delivers an end-to-end detection framework that achieves up to 90% accuracy in classifying leaf wetness under different distances.