Resonance-Based Probability Distribution Modeling presents a novel probabilistic modeling framework predicated on the principles of resonant frequencies and vibrational modes within complex systems. This approach aims to enhance predictive accuracy across diverse domains, including protein folding, fluid dynamics, and other systems exhibiting dynamic behavior. The core mechanism involves constructing a complex, multi-dimensional resonance function to represent system stability and predict outcomes, offering a departure from conventional statistical approaches. This research investigates the potential of this framework to achieve unprecedented levels of predictive capability by leveraging the inherent sensitivity of systems to resonant frequencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach to explainable AI (XAI) within reinforcement learning by leveraging causal reasoning. We model the environment and the agent's policy using a causal Bayesian network. By performing inference through this network, we trace the causal chain of events leading to a specific action, providing a transparent explanation. This method moves beyond simply observing the agent's behavior to understanding the underlying reasons for its choices. The core of our approach lies in identifying and representing the causal relationships within the RL system, enabling us to dissect the decision-making process and ultimately build more robust and reliable RL agents. The proposed framework offers a significant step toward interpretable RL and addresses a critical limitation of current techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neuro-symbolic reasoning by integrating attentive graph neural networks (GNNs) with rule extraction techniques. The core idea is to leverage the pattern recognition capabilities of GNNs for initial data processing while simultaneously extracting symbolic rules that encapsulate domain knowledge. The attentive mechanism within the GNN allows for selective focus on relevant graph features, improving both the accuracy and interpretability of the learned representations. These representations are then used as the basis for logical reasoning and decision-making, providing a more robust and explainable AI system compared to purely neural or symbolic approaches. We demonstrate the effectiveness of this hybrid system through a detailed explanation of the architecture and its theoretical underpinnings. The key contribution lies in the synergistic combination of graph representation learning and symbolic knowledge discovery, resulting in a system capable of handling complex reasoning tasks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neuro-symbolic reasoning by integrating attentive graph neural networks (GNNs) with rule extraction techniques. The core idea is to leverage the pattern recognition capabilities of GNNs for initial data processing while simultaneously extracting symbolic rules that encapsulate domain knowledge. The attentive mechanism within the GNN allows for selective focus on relevant graph features, improving both the accuracy and interpretability of the learned representations. These representations are then used as the basis for logical reasoning and decision-making, providing a more robust and explainable AI system compared to purely neural or symbolic approaches. We demonstrate the effectiveness of this hybrid system through a detailed explanation of the architecture and its theoretical underpinnings. The key contribution lies in the synergistic combination of graph representation learning and symbolic knowledge discovery, resulting in a system capable of handling complex reasoning tasks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Current Explainable AI (XAI) methods frequently deliver post-hoc explanations that lack a fundamental understanding of the causal relationships underpinning AI decision-making. This paper proposes a novel approach to XAI that integrates causal inference and counterfactual analysis, aiming to generate more insightful and actionable explanations. Our methodology leverages causal discovery techniques to identify the key causal factors driving a model's decisions, moving beyond merely highlighting correlations. Furthermore, we employ counterfactual analysis to simulate "what-if" scenarios, allowing us to assess the potential impact of altering specific input features and understand the sensitivity of the model. This approach provides a deeper understanding of the AI system's behavior, ultimately leading to more robust and trustworthy AI models. We demonstrate the potential of this integrated framework through conceptual arguments and a detailed outline of the proposed methodology.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel deep learning model incorporating symbolic reasoning for enhanced visual understanding. The core idea is to bridge the gap between deep learning's ability to extract intricate visual features and symbolic reasoning's capacity for logical deduction. The model consists of two key components: a deep learning module for feature extraction and a symbolic reasoning engine for logical inference. We explore the architecture and training strategies to effectively integrate these components, aiming to achieve more robust and explainable visual understanding. The model is designed to handle tasks requiring not just pattern recognition, but also the ability to interpret relationships and constraints expressed in symbolic form. This work represents a significant step towards more intelligent and reliable AI systems by combining the strengths of both paradigms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significant information loss during model aggregation, a phenomenon addressed by the information bottleneck (IB) principle. This paper proposes a novel framework that integrates the IB technique with differential privacy (DP) within the FL setting. We formulate the problem as a constrained optimization, minimizing information loss while simultaneously satisfying DP guarantees. Our approach utilizes a compressed representation of local data, learned through an IB objective, and introduces noise to protect individual data points, ensuring privacy. The core contribution lies in the synergistic combination of these two techniques, leading to enhanced privacy protection and improved model accuracy compared to standard FL. We demonstrate the effectiveness of our framework through a theoretical analysis and outline potential implementation strategies. The primary goal is to achieve a balance between model performance and privacy preservation, a critical aspect often overlooked in current FL methodologies. The theoretical framework provides a foundation for future research and practical deployment in privacy-sensitive applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject faulty model updates, compromising the global model's integrity. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerant (BFT) consensus protocols. Our system utilizes cryptographic consensus mechanisms to validate and authenticate model updates from each participant before aggregation, thereby mitigating the risks posed by Byzantine nodes. The core innovation lies in the integration of FL with robust BFT consensus, ensuring secure and reliable model training even when faced with adversarial behavior. We introduce a framework utilizing verifiable computation and consensus-based proofs to achieve this. This approach allows for the detection and rejection of malicious updates, ultimately leading to a more trustworthy and resilient global model. The system is designed for scalability and adaptability, addressing key challenges in practical FL deployments. The key mathematical concepts underlying the system are represented through the following notation: (x_i, m_i), where x_i represents the data sample from participant i, and m_i represents the model update generated by participant i. The aggregation function is denoted as (Σ_{i=1}^K (α_i * m_i)), where α_i represents the learning rate for participant i, and K is the total number of participants. The BFT consensus protocol relies on a threshold number of participants (T) to reach agreement, and the proof of correctness is represented as P(m_true, m_agg), where m_true is the true global model, and m_agg is the aggregated model. Byzantine faults are represented as f_i, where f_i is the faulty model update from participant i. The probability of a successful consensus is denoted as P_success. The security level is characterized by the parameter β, representing the probability of successfully detecting a Byzantine update.
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, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches through information leakage and attacks from Byzantine clients attempting to compromise the learning process. This paper proposes a novel distributed FL framework that integrates differential privacy (DP) and Byzantine fault tolerance (BFT) mechanisms to address these challenges comprehensively. Our approach employs advanced DP techniques to rigorously limit the information revealed by individual clients during model updates, while simultaneously utilizing BFT algorithms to detect and mitigate the influence of malicious or faulty clients. The resulting system demonstrates improved security, enhanced privacy guarantees, and robustness against adversarial attacks, making it a significant advancement in the field of secure and reliable distributed learning. The core of our work lies in the synergistic combination of these two crucial techniques, providing a layered defense against potential threats in FL environments. We detail the mathematical formulations underlying our approach and provide a theoretical analysis of its performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks offer a decentralized solution, enabling learning across multiple clients without direct data sharing. The proposed method involves local training of Bayesian Networks on individual client graph subsets, followed by parameter aggregation by a central server to refine a global Bayesian Network model. This architecture addresses the limitations of traditional GNN training while prioritizing data privacy and mitigating computational demands. The key innovation lies in the synergistic combination of federated learning principles with the probabilistic inference capabilities of Bayesian Networks, resulting in a robust and scalable framework for distributed graph learning. This approach demonstrates the potential for efficient learning from decentralized graph data sources.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform computations directly on the encrypted data without ever needing to access the plaintext. Subsequently, the encrypted results are sent back to the participants. This architecture effectively mitigates privacy risks associated with traditional federated learning methods. The combination of these three technologies – federated learning, differential privacy, and homomorphic encryption – provides a powerful framework for secure and decentralized machine learning. We demonstrate the potential for achieving strong privacy guarantees while maintaining the benefits of distributed learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject faulty model updates, compromising the global model's integrity. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerant (BFT) consensus protocols. Our system utilizes cryptographic consensus mechanisms to validate and authenticate model updates from each participant before aggregation, thereby mitigating the risks posed by Byzantine nodes. The core innovation lies in the integration of FL with robust BFT consensus, ensuring secure and reliable model training even when faced with adversarial behavior. We introduce a framework utilizing verifiable computation and consensus-based proofs to achieve this. This approach allows for the detection and rejection of malicious updates, ultimately leading to a more trustworthy and resilient global model. The system is designed for scalability and adaptability, addressing key challenges in practical FL deployments. The key mathematical concepts underlying the system are represented through the following notation: (x_i, m_i), where x_i represents the data sample from participant i, and m_i represents the model update generated by participant i. The aggregation function is denoted as (Σ_{i=1}^K (α_i * m_i)), where α_i represents the learning rate for participant i, and K is the total number of participants. The BFT consensus protocol relies on a threshold number of participants (T) to reach agreement, and the proof of correctness is represented as P(m_true, m_agg), where m_true is the true global model, and m_agg is the aggregated model. Byzantine faults are represented as f_i, where f_i is the faulty model update from participant i. The probability of a successful consensus is denoted as P_success. The security level is characterized by the parameter β, representing the probability of successfully detecting a Byzantine update.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations