This paper explores a novel approach to developing explainable artificial intelligence (AI) by leveraging probabilistic causal graph learning. The core premise is that truly explainable AI requires the identification and representation of genuine causal relationships within data, translated into a readily interpretable probabilistic model. We introduce a mechanism that combines causal inference techniques with deep learning to automatically learn these causal structures. A critical element of this approach is the formalization of "causal uncertainty," providing a quantitative measure of confidence in the learned causal relationships. This framework offers a more robust and trustworthy method for generating explanations compared to traditional methods reliant on correlation alone. The proposed methodology addresses limitations in existing explainable AI techniques by grounding explanations in a verifiable causal structure. We demonstrate the potential of this approach through a theoretical framework, outlining the key components and considerations for successful implementation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, FL systems are vulnerable to Byzantine attacks, where malicious participants introduce corrupted data or models to compromise the learning process. This paper proposes a novel decentralized federated learning framework incorporating Graph Neural Networks (GNNs) for Byzantine fault tolerance. The core idea is to represent the federated learning network as a graph, enabling each participant to learn from neighbors while simultaneously detecting and mitigating the influence of potentially malicious nodes. Our approach employs a GNN to learn node embeddings that capture the relationships within the network, allowing for effective identification of Byzantine nodes based on their anomalous behavior. The dynamic, adaptive nature of this system provides a robust defense against Byzantine attacks, enhancing the reliability and trustworthiness of federated learning systems. We demonstrate the effectiveness of our framework through a theoretical analysis and outline a potential implementation strategy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel decentralized federated learning framework that integrates differential privacy (DP) mechanisms to mitigate privacy risks while maintaining model convergence in a heterogeneous environment. The core of the framework lies in a distributed aggregation strategy and a tailored DP mechanism designed to minimize information leakage from individual client updates. We demonstrate the effectiveness of this approach through a theoretical analysis and outline the key components of the system. The primary goal is to provide a robust and scalable solution for collaborative model training in scenarios where data privacy is paramount and system heterogeneity is significant.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel decentralized federated learning framework that integrates differential privacy (DP) mechanisms to mitigate privacy risks while maintaining model convergence in a heterogeneous environment. The core of the framework lies in a distributed aggregation strategy and a tailored DP mechanism designed to minimize information leakage from individual client updates. We demonstrate the effectiveness of this approach through a theoretical analysis and outline the key components of the system. The primary goal is to provide a robust and scalable solution for collaborative model training in scenarios where data privacy is paramount and system heterogeneity is significant.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clients can significantly impact the fairness and equity of the resulting global model. This paper investigates the propagation and amplification of biases in federated learning systems. We propose a framework for detecting and mitigating these biases by integrating differential privacy and fairness constraints into the training process. The core claim is that FL systems are inherently susceptible to bias propagation. The proposed mechanism involves monitoring model performance across client subgroups, quantifying bias metrics, and adjusting the training process to enforce fairness. This work contributes to the development of more robust and trustworthy FL systems, paving the way for responsible and equitable AI applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clients can significantly impact the fairness and equity of the resulting global model. This paper investigates the propagation and amplification of biases in federated learning systems. We propose a framework for detecting and mitigating these biases by integrating differential privacy and fairness constraints into the training process. The core claim is that FL systems are inherently susceptible to bias propagation. The proposed mechanism involves monitoring model performance across client subgroups, quantifying bias metrics, and adjusting the training process to enforce fairness. This work contributes to the development of more robust and trustworthy FL systems, paving the way for responsible and equitable AI applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of controllable diffusion models to generative design, addressing the limitations of traditional stochastic generative systems. Current design systems often struggle with precise control, leading to unpredictable and potentially undesirable outcomes. We propose a novel framework utilizing diffusion models, which offer a powerful mechanism for guiding the design process through explicit control variables. The core idea is to train a diffusion model to generate designs conditioned on parameters such as shape, material properties, and functional requirements. The iterative nature of the diffusion process allows for designer intervention and refinement, ultimately leading to designs that align with specified constraints and aesthetic preferences. This approach represents a significant advancement in generative design, moving beyond purely stochastic generation towards a more directed and controllable workflow. The key contributions of this work lie in the integration of diffusion models with design objectives, enabling a more intuitive and effective design exploration process. We detail the methodology, including the training process, control variable integration, and the iterative refinement strategy, showcasing the potential of this technology for diverse design applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the temporal dynamics of information flow within social networks, focusing on the propagation of novelty. The core claim is to develop a computational framework to model the spread of information and novelty, providing insights into collective behavior and influence. We propose a system that simulates the propagation of information through a network based on exposure, reciprocity, and social influence, utilizing a dynamic Bayesian network to capture the complex interplay of these factors. The model aims to address current limitations in existing models, particularly in understanding the nuanced dynamics of novelty diffusion. This research utilizes a computational approach to explore how these factors interact over time, ultimately offering a framework for analyzing and predicting the spread of information within social networks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to modeling non-linear self-organization within chaotic systems, utilizing a dynamic self-organizing mechanism. The core of this work is a model based on the principles of chaos theory, employing parameterization and feedback loops to simulate complex system behaviors such as molecular condensation and liquid diffusion. The model incorporates non-linear control to generate intricate patterns. This research aims to provide a framework for understanding and predicting these dynamic systems, moving beyond traditional approaches that often struggle to capture the intricacies of chaotic behavior. The core mechanism centers on establishing a dynamic self-organizing process, driven by parameterization and feedback, which ultimately leads to complex and predictable outcomes. This paper details the model's design, implementation, and results, highlighting the significance of dynamic self-organization in characterizing complex systems. The research demonstrates the effectiveness of this approach in simulating key examples of non-linear self-organization.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel framework for analyzing diffusion fields, incorporating adaptive mixing rules that dynamically adjust the mixing process based on field characteristics. Traditional diffusion models often rely on fixed, static mixing rules, which can limit the realism and stability of the simulations. We introduce a dynamic mixing model that learns the optimal mixing strategy from observed data, resulting in more accurate and robust field behavior. This approach significantly improves the representation of complex diffusion processes, particularly in scenarios with varying field geometries and properties. The core mechanism leverages a neural network to learn the mixing parameters, enabling a more flexible and adaptable framework. The results demonstrate the effectiveness of this approach in simulating a range of diffusion phenomena, showcasing improved stability and accuracy compared to static mixing rules.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the interplay between geometric regularity and convergence within diffusion processes, focusing on the development of a theoretical framework to analyze long-term behavior. The core claim is to create a model that quantitatively assesses the convergence of diffusion, leveraging insights from fractal geometry to quantify divergence and ultimately, ensure convergence. We propose a novel approach that integrates geometric analysis with diffusion theory, providing a robust method for understanding and predicting the evolution of these processes. Long-term behavior is examined through a detailed analysis of the relationship between geometric regularity and convergence, demonstrating its potential to unlock new understandings of complex diffusion systems. The investigation will consider various geometries and diffusion rates, offering a comprehensive analysis that extends beyond simple numerical simulations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Contextualized Semantic Graph Neural Networks (CSGNNs), a novel approach to graph representation learning that addresses the inherent limitations of static graph embeddings. Existing graph neural networks often struggle to adapt to evolving semantic relationships within graphs, primarily due to their inability to capture dynamic changes in node and edge meanings. CSGNNs propose a framework incorporating dynamic contextualization layers to learn and represent relationships based on the evolving semantic meaning. This is achieved through the integration of attention mechanisms trained on external knowledge graphs, providing a rich source of semantic information, and the incorporation of temporal modeling to capture changes in these relationships over time. The core claim is that augmenting standard Graph Neural Networks with these dynamic contextualization layers will significantly improve their performance in tasks requiring understanding of evolving graph structures. The resulting model demonstrates superior ability to learn and represent complex, dynamic relationships compared to static embedding methods. The key innovation lies in the ability to model semantic shifts, leading to more robust and accurate graph representations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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