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. Traditional approaches often struggle with knowledge representation and the ability to handle uncertainty. This work addresses these limitations by constructing a system where HBNs are used to represent and reason about knowledge hierarchically. Neural networks are employed to learn specific features and relationships within the HBN structure, while the Bayesian network provides a framework for probabilistic inference and reasoning under uncertainty. The hierarchical structure enables the system to decompose complex problems into smaller, more manageable sub-problems, improving both accuracy and interpretability. This approach demonstrates the potential to create more robust and explainable AI systems capable of handling complex reasoning tasks. The key contribution is the specific application of HBNs for this integration, moving beyond simple neural-symbolic hybrids. We outline the architecture, learning process, and inference mechanisms, focusing on the benefits of a structured knowledge representation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 interpretable insights into AI-driven security decisions. The core claim is that building an XAI-based system for security threat detection and defense will improve both the efficiency and reliability of security measures. The proposed mechanism utilizes XAI to elucidate the reasoning behind AI's judgments, thereby facilitating the identification of vulnerabilities and potential threats. Specifically, we explore methods for generating explanations that highlight critical factors influencing security decisions, allowing human analysts to validate, refine, or override AI recommendations. This approach addresses the limitations of current black-box AI security systems by incorporating human understanding and control, ultimately leading to a more robust and trustworthy security posture. The research contributes to a paradigm shift in system security, moving beyond solely relying on AI's predictive capabilities to actively incorporating human expertise within the decision-making process.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 employed to explicitly model the causal relationships between input features and the model's output, offering a more robust and interpretable explanation. Unlike existing XAI methods which often rely on correlations, our approach leverages causality to provide a truly grounded understanding of how the model arrives at its decisions. The method is presented with a detailed theoretical framework and outlines the steps involved in constructing and utilizing CBNs for explainability. This research addresses a critical limitation in current XAI by moving beyond correlation-based explanations towards a causal understanding, ultimately leading to more reliable and trustworthy AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 information-theoretic framework to address this limitation. We define a "Privacy Loss Function" predicated on mutual information between local models and global updates, providing a quantifiable measure of information leakage. The framework leverages established techniques such as differential privacy and homomorphic encryption to minimize this loss, ultimately leading to more robust and trustworthy FL systems. Our approach moves beyond intuitive notions of privacy, offering a mathematically sound foundation for designing and analyzing FL protocols, facilitating the development of truly secure and efficient distributed learning solutions. The core contribution is the formalization of privacy risk in FL using information-theoretic principles, enabling a more precise understanding and control over data leakage.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 compromising the global model's integrity. This paper proposes a novel decentralized federated learning system incorporating Byzantine fault tolerance (BFT) achieved through a blockchain-based verification layer. The system leverages blockchain technology to cryptographically verify model updates from each participant before they are aggregated into the global model. This ensures data integrity and enables the detection and mitigation of Byzantine attacks. The core claim is that existing FL systems are vulnerable to these attacks. The proposed mechanism implements a decentralized FL system utilizing a blockchain-based verification layer. This represents a new approach to robust FL training. The system utilizes the following key components: participant nodes, a blockchain network, and a global model aggregator. The blockchain network is composed of multiple nodes that maintain a ledger of all model updates. The global model aggregator utilizes the blockchain network to verify model updates before aggregating them into the global model. The proposed system enhances the security and robustness of FL training by providing a tamper-proof audit trail and enabling the detection of malicious participants.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 information-theoretic framework to address this limitation. We define a "Privacy Loss Function" predicated on mutual information between local models and global updates, providing a quantifiable measure of information leakage. The framework leverages established techniques such as differential privacy and homomorphic encryption to minimize this loss, ultimately leading to more robust and trustworthy FL systems. Our approach moves beyond intuitive notions of privacy, offering a mathematically sound foundation for designing and analyzing FL protocols, facilitating the development of truly secure and efficient distributed learning solutions. The core contribution is the formalization of privacy risk in FL using information-theoretic principles, enabling a more precise understanding and control over data leakage.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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. Traditional Federated Learning (FL) solutions, while offering a degree of data privacy, still rely on centralized aggregation, a point of vulnerability. Our proposed DFLDP framework addresses this limitation by adopting a decentralized architecture where individual researchers maintain complete control over their datasets. Crucially, we integrate differential privacy mechanisms directly into the aggregation process, adding a quantifiable layer of protection against data leakage. This ensures that the learned model benefits from the collective knowledge of multiple researchers without revealing individual data contributions. The system utilizes a gossip-based communication protocol for model updates, minimizing communication overhead. We formally define the mathematical framework, outlining the key components and their interactions. The system's performance is evaluated in a simulated environment, demonstrating the effectiveness of the DFLDP approach in achieving accurate models while upholding stringent privacy guarantees. The core claim of this work is that sharing raw scientific data for federated learning is often prohibited due to privacy concerns and intellectual property restrictions. The core mechanism implemented is the realization of a federated learning system that utilizes differential privacy to protect data during aggregation, while also employing a decentralized architecture where individual researchers retain control over their data. This new approach combines federated learning with differential privacy and decentralization, enabling collaborative scientific discovery without compromising data privacy or intellectual property rights.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 framework to provide an enhanced level of privacy protection. We introduce a novel approach where noise is injected into model parameters during the training process, adhering to the principles of DP. This mechanism mitigates the risk of information leakage from individual user data while maintaining model accuracy. We formally define the proposed algorithm and analyze its privacy-utility trade-off. The results demonstrate that the incorporation of DP significantly strengthens the privacy guarantees of FL, offering a robust solution for privacy-sensitive applications. Our work contributes to the growing body of research focused on secure and privacy-preserving FL techniques.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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). This framework addresses the limitations of traditional federated learning, particularly when dealing with graph-structured data, where raw data sharing poses significant privacy concerns. The system avoids direct data exchange by allowing devices to collaboratively learn through the exchange of model updates, preserving data privacy while enabling effective knowledge transfer. The proposed FedGNN architecture consists of local graphs and locally trained GNNs, synchronized via a novel aggregation mechanism. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and outline the key components required for implementation. The system is designed to be scalable and adaptable to various decentralized network topologies. This work represents a significant step towards privacy-preserving collaborative learning in complex, graph-based environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
