## 5 Results and Discussion
### Dataset
The dataset comprises images from five distinct classes, each corresponding to a specific rice leaf disease. These classes are blast, blight, brown spot, leaf smut, and tungro. Each class is represented by a varying number of images, contributing to the diversity and richness of the dataset. The distribution of images among classes is as follows: blast (80 images), blight (80 images), brown spot (40 images), leaf smut (40 images), and tungro (80 images). The dataset encompasses 320 images, providing a comprehensive and well-balanced representation of rice leaf diseases for effective model training and evaluation. As shown in Figure 2, samples of the rice leaf disease dataset highlight the visual representation of the different classes. * WaRP dataset [46]: This dataset is designed to address the need for an extensive collection of images for training and testing the capabilities of waste recycling plants in automatically sorting recyclable items on conveyor belts. The dataset is specifically tailored to recognize and classify various waste categories found in recycling plants, including plastic and glass bottles, cardboard, detergents, canisters, and cans. The dataset, WaRP (waste recycling plant), consists of manually labeled images taken from an industrial conveyor. It encompasses 28 recyclable waste categories, with objects classified into groups such as plastic bottles, glass bottles, cardboard, detergents, canisters, and cans. Differentiation among categories is essential, considering the specific recycling technologies associated with each. Unique features of the WaRP dataset include indicating whether a plastic bottle is filled with air (\"full\" postfix), which is crucial for correctly operating the conveyor's manipulator. Unlike other datasets, the WaRP dataset allows for object overlap, heavy deformation, and variations in lighting conditions, simulating real-world scenarios encountered in recycling plants. It is worth noting that the dataset is intentionally unbalanced, mirroring the naturally uneven distribution of household waste on a conveyor belt due to the varying frequency of use of different objects. For instance, there are more bottle images than canisters in the dataset. The WaRP dataset is divided into three parts: WaRP-D for training and quality assessment of detection, WaRP-C for training and objective quality assessment of classification, and WaRP-S for validating weakly supervised segmentation methods. Each part serves a specific purpose, contributing to the overall effectiveness of the dataset in training and evaluating waste recognition models. The dataset statistics reveal the distribution of images across different categories, providing insights into the number of training and testing samples for each waste category in the WaRP-D and WaRP-C parts. This comprehensive dataset aims to facilitate developing and testing methods for detecting, classifying, and segmenting waste in recycling plants, addressing the specific challenges encountered in real-world scenarios. As illustrated in Figure 3, dataset samples visually depict the variety of waste categories in the WaRP-C part. It is important to note that the data partitioning strategy used in the proposed FedLO framework is crucial to the training process. Essentially, the training data are divided into subsets at random, ensuring that each client receives a diverse portion of the dataset for training. This method fosters collaborative learning while still maintaining data privacy. An FL strategy is employed to handle heterogeneity in data distribution among clients. Each client trains their model independently using their local data subset. This decentralized approach enables clients to use their local data while also contributing to enhancing the global model. Additionally, model aggregation at the server allows for collaborative learning, which helps the model benefit from the collective knowledge of all clients. Figure 2: Samples of rice leaf disease dataset.