Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems often struggle with privacy concerns and convergence issues, particularly when dealing with heterogeneous data distributions across clients. This paper proposes a novel federated learning algorithm that integrates differential privacy (DP) with standard FL to achieve both strong privacy guarantees and improved convergence performance. The algorithm utilizes a carefully designed aggregation strategy and adaptive learning rates to mitigate the effects of data heterogeneity. We demonstrate the effectiveness of this approach through theoretical analysis and simulations, highlighting its ability to balance privacy, accuracy, and convergence speed. Our contributions include a new federated learning framework incorporating DP and addressing data heterogeneity challenges, potentially leading to more robust and reliable FL systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Abstract: Develop a new method for efficiently modeling stochastic diffusion processes within deep neural networks, improving the stability and generalization of these models. Core Claim: Develop a new method for efficiently modeling stochastic diffusion processes within deep neural networks, improving the stability and generalization of these models. Core Mechanism: Employ a Bayesian approach to estimate the loss function in the diffusion process, ensuring a robust and reliable model. Why It Is New: Addresses a significant challenge in deep learning, improving the stability of complex models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to graph structure learning based on adaptive neural networks. Traditional graph neural networks often exhibit static structure, failing to effectively adapt to dynamic changes in the graph. We propose a method that utilizes an adaptive neural network to dynamically adjust the graph structure, thereby enhancing graph performance. The core mechanism involves a self-adaptive learning process that continuously updates the graph's connectivity and node features based on observed changes. The algorithm is evaluated on several benchmark graph datasets, demonstrating significant improvements in performance metrics compared to existing methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Cybersecurity threats are increasingly complex and dynamic, posing significant challenges to traditional detection methods. This paper proposes the application of Graph Neural Networks (GNNs) to enhance cybersecurity threat detection by leveraging the inherent relational nature of network traffic data. We construct a graph structure where nodes represent devices and users, and edges represent communication patterns. A GNN is then trained to learn complex behavioral patterns associated with malicious activity, enabling accurate threat identification. Our approach offers a novel method for analyzing intricate network data, potentially improving detection rates and reducing false positives compared to conventional techniques. The core claim is that GNNs can effectively model and analyze network traffic to detect cyber threats. The core mechanism involves constructing a graph based on network communication and training a GNN to identify anomalous behaviors. This represents a new approach to cybersecurity threat detection.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The design of effective Graph Neural Networks (GNNs) is a complex and often iterative process, frequently relying on expert intuition and extensive experimentation. This work introduces a novel approach leveraging meta-learning to automate the architecture design of GNNs. Our core claim is that a meta-learning model can learn to optimize GNN architectural parameters, given a limited number of task-specific examples. This approach significantly reduces the design time and potentially leads to architectures tailored for specific graph-structured data. We define the problem as learning an architecture search strategy, utilizing meta-learning to train a model that can efficiently identify optimal GNN configurations for various downstream tasks. The key mechanism involves training a meta-learner to predict architectural parameters such as hidden layer sizes, activation functions, and aggregation operations based on task-specific data. This allows for rapid prototyping and adaptation to new graph datasets without requiring extensive manual tuning. We demonstrate the effectiveness of this approach through a series of simulations, showcasing the ability of the meta-learner to discover architectures that outperform traditional, manually designed GNNs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and learning from graph-structured data, finding applications in diverse domains such as social network analysis, drug discovery, and recommendation systems. However, the training of GNNs presents unique privacy challenges due to the intricate relationships between nodes within a graph. Directly applying standard differential privacy techniques to GNNs can lead to significant information leakage, effectively disrupting the learned graph structure and potentially compromising node relationships. This work introduces a novel approach to differentially private GNN training that aims to mitigate these challenges. Our methodology focuses on preserving both the overall graph connectivity and the specific relationships between nodes while simultaneously providing robust privacy guarantees. We formulate the problem as a constrained optimization, considering both the privacy loss and the graph reconstruction error. The key innovation lies in a carefully designed aggregation mechanism that incorporates noise in a way that minimizes the impact on graph structure. The theoretical analysis demonstrates the feasibility of achieving a desired privacy level while maintaining a reasonable level of accuracy. Experimental results, though limited to synthetic datasets for this initial exploration, suggest the potential of our approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of dynamic graph theory to machine learning, specifically focusing on the design and implementation of adaptive graph neural networks (AGNNs). Traditional neural network architectures often struggle with data that evolves over time, necessitating robust and adaptable learning strategies. Dynamic graph theory provides a powerful framework for understanding and representing data structure, enabling AGNNs to dynamically adjust their parameters based on the data. We propose a novel AGNN framework incorporating dynamic graph theory, leveraging the concept of node and edge weights to capture the relationships within the data. The core mechanism revolves around a mechanism that dynamically adjusts the network's parameters based on the observed graph structure, ensuring optimal feature extraction and model learning. This approach significantly improves the performance of machine learning models when dealing with evolving datasets.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel graph neural network (GNN) architecture, dubbed Dynamic Topology Graph Neural Network (DTGNN), that leverages dynamic topology to enhance modeling capabilities. Traditional GNNs often struggle with non-linear data due to static graph representations. The DTGNN dynamically adjusts graph connections through iterative optimization, allowing the model to actively adapt to environmental changes and improve accuracy. This work presents a framework for robust and adaptable graph representation learning, offering a significant advancement over static graph models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) to symbolic logic reasoning. Traditional methods for symbolic logic inference often rely on heuristic algorithms and rule engines, which can be inefficient, particularly for complex logical systems. We propose a novel framework that leverages the structural representation capabilities of GNNs to model and solve symbolic logic problems effectively. The core idea involves transforming symbolic logic formulas into graph structures, enabling GNNs to learn embeddings that capture the relationships within the logic. These embeddings are then utilized to perform logical inference tasks, such as deriving new formulas or validating formula validity. This approach offers a potentially more scalable and efficient solution compared to existing methods. The key contributions of this work lie in the integration of graph representation learning with symbolic logic reasoning, providing a new computational paradigm for this long-standing problem.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to probabilistic programming by leveraging the inherent capabilities of neuromorphic architectures. Traditional probabilistic programming often relies on manually constructed probability transition graphs, a process that can be both time-consuming and limiting in complex scenarios. This research introduces a system that utilizes the synaptic plasticity of spiking neural networks (SNNs) to automatically learn and optimize program execution paths. The core mechanism employs an energy minimization algorithm to refine the SNN's connectivity, effectively mimicking probabilistic transitions within the program. This approach offers the potential for increased robustness, adaptability, and potentially reduced computational overhead compared to conventional probabilistic programming methods. The system demonstrates a framework for translating symbolic programs into a biologically plausible computational model, achieving learning and adaptation through intrinsic neural dynamics. The presented methodology represents a shift from explicit graph construction to implicit neural learning, aligning with the strengths of neuromorphic computing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, traditional GNNs often treat edges as static features, failing to capture the dynamic and contextual nature of relationships within a graph. This paper introduces Relational Graph Neural Networks with Contextualized Edge Embeddings (RGNNC), a novel approach that addresses this limitation. RGNNC leverages recurrent neural networks to generate dynamic edge embeddings, considering both node features and neighborhood information, thereby providing a context-aware representation of graph relationships. The core claim of this work is that enhancing GNNs with these contextualized edge embeddings significantly improves their performance. The mechanism involves training a recurrent neural network to predict edge features based on the surrounding network structure and temporal evolution of nodes. This dynamic representation allows RGNNC to better capture complex relational patterns and achieve superior results compared to static edge embedding approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) is a cornerstone of artificial intelligence, aiming to mechanize mathematical reasoning. However, traditional ATP systems often struggle with scalability and efficiency, particularly for complex theorems. This paper proposes a novel approach utilizing Graph Neural Networks (GNNs) to address these limitations by transforming theorem proving into a distributed graph reasoning problem. We introduce a framework where logical statements are represented as graphs and a GNN is trained to perform parallel inference across multiple nodes in a distributed system. The core claim is that GNNs can significantly accelerate and scale ATP by enabling efficient parallel reasoning. The proposed method leverages the inherent graph representation capabilities of GNNs to capture relationships between logical components, facilitating automated deduction and inference. This approach offers a promising direction for future advancements in ATP, potentially leading to more robust and scalable automated reasoning systems. The effectiveness of this approach is demonstrated through theoretical analysis and conceptual design, outlining the key components and functionalities of the proposed system.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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