This paper proposes a novel approach to distributed knowledge reasoning by leveraging cognitive maps. The core idea is to represent knowledge as a structured graph, where nodes represent concepts and edges represent relationships. This graph representation facilitates efficient and reliable reasoning through the application of graph algorithms. Furthermore, we integrate distributed computing techniques to enable knowledge sharing and updates across a distributed system. The system's architecture is designed to enhance both the speed and interpretability of knowledge inference. We demonstrate the potential of this approach in a distributed environment, aiming for a more robust and scalable solution for knowledge reasoning tasks. The key contributions lie in the structured representation of knowledge using cognitive maps and the utilization of graph algorithms for reasoning, leading to improved efficiency and explainability compared to traditional methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without direct data sharing. However, ensuring the integrity and accuracy of these models remains a significant challenge. Traditional verification methods often rely on centralized aggregation, inherently compromising user privacy and introducing a single point of failure. This paper proposes a novel blockchain-based system for decentralized verification of FL models. The system leverages cryptographic proofs, specifically zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs), allowing nodes to independently verify model updates without revealing the underlying data. This approach eliminates centralized aggregation, enhancing privacy and establishing a verifiable, distributed ledger of model updates. The core claim is that current decentralized verification methods are computationally expensive and rely on centralized aggregation, thus, this system offers a truly decentralized and privacy-preserving verification framework.
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 the data itself. However, the inherent privacy risks associated with aggregating model updates introduce significant challenges. Traditional differential privacy (DP) techniques often rely on adding uniform random noise to gradients, which can be overly conservative and degrade model accuracy. This paper introduces an adaptive noise scheme for FL that dynamically adjusts the noise level based on the sensitivity of the aggregated gradients. The proposed method monitors gradient variance and employs a stochastic gradient descent (SGD) variant with a dynamically adjusted learning rate and noise scale. We demonstrate through theoretical analysis and a simplified simulation that this approach significantly reduces the overall noise level compared to standard DP while maintaining a comparable privacy guarantee, ultimately leading to improved model accuracy in FL settings. The key contribution lies in the intelligent adaptation of noise, responding directly to the data's inherent characteristics.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without direct data sharing. However, ensuring the integrity and accuracy of these models remains a significant challenge. Traditional verification methods often rely on centralized aggregation, inherently compromising user privacy and introducing a single point of failure. This paper proposes a novel blockchain-based system for decentralized verification of FL models. The system leverages cryptographic proofs, specifically zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs), allowing nodes to independently verify model updates without revealing the underlying data. This approach eliminates centralized aggregation, enhancing privacy and establishing a verifiable, distributed ledger of model updates. The core claim is that current decentralized verification methods are computationally expensive and rely on centralized aggregation, thus, this system offers a truly decentralized and privacy-preserving verification framework.
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 intentionally submit incorrect or misleading updates, compromising the integrity and accuracy of the global model. This paper proposes a novel decentralized federated learning system that integrates Byzantine fault tolerance and a Proof-of-Stake (PoS) incentive mechanism to mitigate these vulnerabilities. The core idea is to incentivize participants to contribute honest updates while simultaneously providing cryptographic proofs to verify the validity of these updates. This system constructs a decentralized network where participants stake resources (e.g., computational power or storage) to gain voting rights and rewards for contributing correct updates. Malicious behavior is penalized through a reduction in stake and potentially exclusion from the network. The system utilizes cryptographic techniques, such as zero-knowledge proofs, to ensure the integrity of updates without revealing the underlying data. This approach significantly enhances the robustness of FL systems against adversarial attacks and promotes a more trustworthy and reliable learning environment. The proposed system addresses the critical challenge of Byzantine fault tolerance in FL, offering a practical and scalable solution for deploying FL models in sensitive environments.
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 the data itself. However, the inherent privacy risks associated with aggregating model updates introduce significant challenges. Traditional differential privacy (DP) techniques often rely on adding uniform random noise to gradients, which can be overly conservative and degrade model accuracy. This paper introduces an adaptive noise scheme for FL that dynamically adjusts the noise level based on the sensitivity of the aggregated gradients. The proposed method monitors gradient variance and employs a stochastic gradient descent (SGD) variant with a dynamically adjusted learning rate and noise scale. We demonstrate through theoretical analysis and a simplified simulation that this approach significantly reduces the overall noise level compared to standard DP while maintaining a comparable privacy guarantee, ultimately leading to improved model accuracy in FL settings. The key contribution lies in the intelligent adaptation of noise, responding directly to the data's inherent characteristics.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to distributed machine learning, enabling model training on decentralized devices without directly exchanging sensitive data. However, the inherent vulnerability of FL systems to Byzantine failures – where malicious participants intentionally corrupt the learning process – poses a significant threat to its reliability and security. This paper proposes a decentralized machine learning framework leveraging federated learning with Byzantine fault tolerance (BFT). The core claim is the development of a robust FL system capable of maintaining accurate and secure model training despite the presence of adversarial actors. The proposed mechanism utilizes verifiable computation and secret sharing techniques to mitigate the impact of Byzantine attacks. This work addresses a critical gap in existing FL research by explicitly tackling the security challenges associated with adversarial behavior, paving the way for more trustworthy and resilient distributed learning applications. The system is designed to ensure data privacy and model integrity, even when compromised by malicious nodes. The theoretical framework and the proposed architecture will be discussed in detail.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to distributed machine learning, enabling model training on decentralized devices without directly exchanging sensitive data. However, the inherent vulnerability of FL systems to Byzantine failures – where malicious participants intentionally corrupt the learning process – poses a significant threat to its reliability and security. This paper proposes a decentralized machine learning framework leveraging federated learning with Byzantine fault tolerance (BFT). The core claim is the development of a robust FL system capable of maintaining accurate and secure model training despite the presence of adversarial actors. The proposed mechanism utilizes verifiable computation and secret sharing techniques to mitigate the impact of Byzantine attacks. This work addresses a critical gap in existing FL research by explicitly tackling the security challenges associated with adversarial behavior, paving the way for more trustworthy and resilient distributed learning applications. The system is designed to ensure data privacy and model integrity, even when compromised by malicious nodes. The theoretical framework and the proposed architecture will be discussed in detail.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum-Enhanced Graph Neural Networks (QEN) presents a novel approach to graph neural network (GNN) learning, leveraging the principles of quantum entanglement to enhance both the training process and the network's expressive capabilities. This research investigates a method for utilizing entanglement to accelerate training and improve the network's ability to represent complex relationships within graph data. We introduce a new architecture incorporating quantum entanglement-enhanced layer activations, exploring its potential to overcome limitations inherent in classical GNN training. The core mechanism focuses on utilizing entanglement to reduce computational complexity and improve the speed of learning. This work demonstrates promising results through theoretical analysis and preliminary experimental validation, suggesting a significant improvement in both training speed and model performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Graph Neural Networks (GNNs) for program vulnerability prediction. Traditional methods for vulnerability detection often rely on static analysis and signature-based approaches, which can be limited in their ability to capture complex code relationships and identify novel vulnerabilities. This research proposes a novel framework that leverages the power of GNNs to learn structural information from program code, ultimately leading to improved vulnerability prediction accuracy. The core idea is to represent program code as a graph, where nodes represent code elements (e.g., statements, functions, variables) and edges represent relationships between them. GNNs are then trained on this graph to identify patterns indicative of vulnerabilities, such as insecure coding practices and potential attack vectors. The results demonstrate the effectiveness of this approach, showcasing the potential of GNNs to enhance vulnerability detection and risk assessment in software development. The key contributions of this work include the development of a graph-based representation for program code, the design of a tailored GNN architecture for vulnerability prediction, and the demonstration of improved prediction accuracy compared to existing methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel optimization framework based on graph neural networks (GNNs) designed for geometric transformation optimization. Traditional optimization methods often rely on handcrafted objective functions and are limited in their ability to handle complex geometric transformations. This work leverages the power of GNNs to learn a mapping of geometric transformations, enabling automated optimization. We propose a method that utilizes a graph representation of the transformation space, where nodes represent geometric elements and edges represent the transformations applied to them. A GNN is trained to predict the optimal transformation sequence, allowing for efficient and robust optimization of complex geometric patterns. The core mechanism focuses on learning a robust representation of the transformation space through graph neural networks, facilitating the discovery of optimal geometric transformations. The paper demonstrates the effectiveness of this approach through comprehensive experiments on several challenging geometric transformation scenarios, highlighting its superior performance compared to traditional optimization techniques. The results underscore the potential of GNNs for automating geometric transformation optimization, particularly in scenarios involving intricate patterns and high-dimensional transformations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel Multi-Modal Knowledge Graph Reasoning Engine designed to facilitate complex reasoning and knowledge discovery through the interactive integration of diverse knowledge graph modalities. The core claim is that leveraging the interplay between multi-modal knowledge graphs significantly enhances the ability to perform sophisticated reasoning tasks compared to traditional, single-modality approaches. The proposed mechanism centers around constructing a fused knowledge graph encompassing textual, visual, and auditory data, coupled with a graph neural network (GNN)-based reasoning engine. This architecture overcomes the limitations inherent in single-modality knowledge graphs, offering a more comprehensive and nuanced understanding of interconnected information. The engine is designed to dynamically adapt to the relationships between different modalities, improving the accuracy and efficiency of reasoning processes. The paper outlines the architecture, the GNN implementation, and potential applications, illustrating a pathway toward intelligent systems capable of sophisticated knowledge extraction and inference.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations