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This paper proposes a novel neuro-symbolic reasoning framework based on Hierarchical Bayesian Networks (HBNs). The core challenge in neuro-symbolic reasoning lies in effectively integrating the pattern recognition capabilities of neural networks with the structured reasoning capabilities of symbolic systems. Traditiona...
This paper investigates the application of Explainable Artificial Intelligence (XAI) to enhance system security. Traditional security systems often rely on opaque "black box" AI models, hindering effective threat detection and defense. This work proposes a system architecture leveraging XAI techniques to provide interp...
Current Explainable AI (XAI) techniques frequently generate explanations that are merely post-hoc justifications for model predictions, lacking a deep understanding of the model's reasoning process. This paper proposes a novel approach to XAI based on the construction of Causal Bayesian Networks (CBNs). CBNs are employ...
Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an informat...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately com...
Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an informat...
This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Trad...
Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data fro...
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framewor...
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This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a frame...
This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...
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