KEYWORDS: Data modeling, Computer security, Visualization, Neurons, Instrument modeling, Data communications, Acoustics, Profiling, Data acquisition, Visual process modeling
Secure data communication is crucial in contested environments such as battlefields. In such environments, there is always risk of data breach through unauthorized interceptions. This may lead to unauthorized access to tactical information and infiltration into the systems. In this work, we propose a detailed training setup in the federated learning framework for object classification where the raw data will be maintained locally at the edge devices and will not be shared with a central server or with each other. The server sends a global model to edge devices, which is then trained locally at the edge, and the updated parameters are sent back to the central server, where they are aggregated, which takes place iteratively. This setup ensures robustness against malicious cyberattacks as well as reduce communication overhead. Furthermore, to tackle the irregularity in object classification task with a single data modality in such contested environment, a deep learning model incorporating multiple modalities is used as the global model in our proposed federated learning setup. This model can serve as a possible solution in object identification with multi-modal data. We conduct a comprehensive analysis on the importance of multi-modal approach compared to individual modalities within our proposed federate learning setup. We also provide a resource profiling based on memory requirements, training time, and energy usage on two resource constrained devices to demonstrate the feasibility of the proposed approach.
Deep Learning (DL) requires a massive, labeled dataset for supervised semantic segmentation. Getting massive labeled data under a new setting (target domain) to perform semantic segmentation requires huge efforts in time and resources. One possible solution is domain adaptation (DA) where researchers transform the data distribution of existent annotated public data (source domain) to resemble the target domain. We develop a model on this transformed data. Nevertheless, this poses the questions of what source domain/s to utilize, and what types of transformation to perform on that domain/s. In this research work, we study those answers by benchmarking different data transformation approaches on source-only and single-source domain adaptation setups. We provide a new well-suited dataset using unmanned ground vehicle Husarion ROSbot 2.0 to analyze and demonstrate the relative performance of different DA approaches.
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