This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidentiality. Federated learning offers a decentralized alternative, training models across distributed devices without direct data sharing. However, inherent vulnerabilities remain due to the model updates themselves potentially revealing sensitive information. This work addresses this challenge by introducing a framework that incorporates differential privacy mechanisms directly into the federated learning process. We detail a proposed algorithm that adds calibrated noise to model updates, ensuring that the influence of any single patient's data on the global model is limited. The core contribution is a novel approach to balancing the privacy guarantees of DP with the utility requirements of FL, particularly within the sensitive domain of healthcare. The results demonstrate the feasibility of achieving privacy-preserving personalized healthcare models and highlight potential avenues for future research. Mathematical notation and formulas are presented in plain text for seamless copy-pasting.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to integrating differential privacy into federated learning (FL) systems. The core challenge in FL lies in protecting user data while still achieving high model accuracy. This work introduces a distributed differential privacy (DDP) mechanism specifically designed to address this challenge. Our method utilizes a local perturbation strategy at each client, combined with a global privacy accounting protocol. This approach guarantees differential privacy at the client level, minimizing the risk of individual data disclosure, without incurring a significant drop in model accuracy. We demonstrate that this tailored DDP solution offers a more sophisticated and effective approach compared to existing methods, providing a robust framework for privacy-preserving FL. The key contributions of this paper are the design of the local perturbation strategy and the global privacy accounting protocol, both optimized for the unique constraints of the federated learning setting. This results in a system that balances privacy protection and model utility.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) presents a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, the inherent vulnerability of FL to adversarial attacks poses a significant threat to its security and reliability. This paper proposes a novel approach to secure FL by leveraging hyperdimensional computing (HDC). HDC utilizes high-dimensional vectors with near-binary values, offering inherent robustness against noise and providing a strong foundation for secure aggregation and communication. The core claim is that HDC's properties provide a robust defense against gradient manipulation, a common attack vector in FL. The proposed mechanism utilizes HDC's capacity for complex pattern recognition to create a resilient aggregation process. This approach represents a fundamentally new strategy for securing FL, diverging from conventional cryptographic solutions. We outline the key components of the HDC-based FL system, focusing on the design of the HDC vectors, the aggregation protocol, and the validation process. The system is designed to minimize the impact of adversarial gradients while maintaining model accuracy. We demonstrate the potential of HDC to significantly enhance the security and robustness of FL systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the potential of non-Euclidean diffusion processes to revolutionize material science. Traditional approaches often assume uniform material properties, limiting our ability to precisely tailor material structures. We propose a novel mathematical framework based on a non-Euclidean diffusion equation, aiming to predict and control the formation of complex material architectures. This research will investigate the impact of non-Euclidean diffusion on material properties, offering a fundamentally new approach to material design and optimization. The core mechanism involves constructing a dynamic model that governs the evolution of material structures, enabling the prediction of novel material characteristics. The potential applications span diverse fields, including nano-fabrication, structural materials, and advanced optical properties. This work lays the groundwork for a paradigm shift in materials design, moving beyond simple material properties towards a deeper understanding of the underlying processes shaping material behavior.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a formal mathematical model for simulating information diffusion within social networks utilizing an agent-based simulation (ABS) approach. The core aim is to provide a rigorous framework for understanding the complex dynamics of how information spreads through interconnected social groups. The model incorporates key elements including agent characteristics, network topology, social influence dynamics, and information credibility, represented through mathematical equations. The simulation allows for the exploration of various scenarios and parameters to assess their impact on the overall diffusion process. The results highlight the significance of network structure and individual agent attributes in determining the speed and extent of information dissemination. The model's formalization offers a quantifiable approach to studying information diffusion, moving beyond qualitative observations to predictive analysis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Counterfactual Diffusion Networks (CDN), a novel approach to causal representation learning. Traditional representation learning methods often fail to accurately capture underlying causal relationships within data, leading to issues in downstream tasks that rely on understanding these relationships. CDN addresses this limitation by employing a diffusion model to simulate counterfactual scenarios. The core idea is to learn representations that reflect how changes to one variable propagate through the system, effectively capturing causal dependencies. The diffusion process allows the network to learn robust representations even in the presence of noise and confounding variables. We demonstrate that CDN outperforms existing correlation-based methods in capturing causal structure and improves the accuracy of causal inference tasks. The key contributions of this work are the integration of diffusion models with counterfactual reasoning and the demonstration of CDN's effectiveness in learning causal representations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a formal mathematical model for simulating information diffusion within social networks utilizing an agent-based simulation (ABS) approach. The core aim is to provide a rigorous framework for understanding the complex dynamics of how information spreads through interconnected social groups. The model incorporates key elements including agent characteristics, network topology, social influence dynamics, and information credibility, represented through mathematical equations. The simulation allows for the exploration of various scenarios and parameters to assess their impact on the overall diffusion process. The results highlight the significance of network structure and individual agent attributes in determining the speed and extent of information dissemination. The model's formalization offers a quantifiable approach to studying information diffusion, moving beyond qualitative observations to predictive analysis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the potential of non-Euclidean diffusion processes to revolutionize material science. Traditional approaches often assume uniform material properties, limiting our ability to precisely tailor material structures. We propose a novel mathematical framework based on a non-Euclidean diffusion equation, aiming to predict and control the formation of complex material architectures. This research will investigate the impact of non-Euclidean diffusion on material properties, offering a fundamentally new approach to material design and optimization. The core mechanism involves constructing a dynamic model that governs the evolution of material structures, enabling the prediction of novel material characteristics. The potential applications span diverse fields, including nano-fabrication, structural materials, and advanced optical properties. This work lays the groundwork for a paradigm shift in materials design, moving beyond simple material properties towards a deeper understanding of the underlying processes shaping material behavior.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum computing promises revolutionary advancements across various fields, yet its practical realization is severely hampered by errors arising from various sources. Traditional approaches to error correction in quantum computing often treat decoherence and other error mechanisms as isolated processes. This paper proposes a novel framework for understanding and mitigating quantum computing errors by grounding them within the principles of non-equilibrium thermodynamics. We argue that quantum bit (qubit) errors are not solely due to decoherence, but are fundamentally linked to energy transfer and entropy generation within the quantum system. We develop a mathematical model based on non-equilibrium thermodynamics to describe the propagation of errors, considering the dynamic interactions between qubits and their environment. This model incorporates concepts such as the Boltzmann equation, the Fokker-Planck equation, and the Landauer principle. Specifically, we define error propagation as a non-linear diffusion process driven by thermal fluctuations and dissipation. The model predicts the evolution of the error distribution across a quantum processor, highlighting the critical role of temperature, coupling strengths, and dissipation rates in error propagation. Furthermore, we explore potential control strategies based on this thermodynamic framework, including tailored cooling schemes and optimized qubit interactions to minimize error propagation. The core claim of this work is that a comprehensive understanding of quantum computing errors necessitates a shift from purely quantum mechanical descriptions to a thermodynamic perspective, offering a pathway towards more robust and scalable quantum computers.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Counterfactual Diffusion Networks (CDN), a novel approach to causal representation learning. Traditional representation learning methods often fail to accurately capture underlying causal relationships within data, leading to issues in downstream tasks that rely on understanding these relationships. CDN addresses this limitation by employing a diffusion model to simulate counterfactual scenarios. The core idea is to learn representations that reflect how changes to one variable propagate through the system, effectively capturing causal dependencies. The diffusion process allows the network to learn robust representations even in the presence of noise and confounding variables. We demonstrate that CDN outperforms existing correlation-based methods in capturing causal structure and improves the accuracy of causal inference tasks. The key contributions of this work are the integration of diffusion models with counterfactual reasoning and the demonstration of CDN's effectiveness in learning causal representations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel framework for constructing and evolving a neuro-symbolic knowledge graph (NSKG) capable of adapting to dynamic changes in information. The core idea is to combine the strengths of deep learning and knowledge graphs, leveraging graph neural networks (GNNs) to learn relationships within the graph and incorporating a reward mechanism to guide optimization towards higher graph quality. Furthermore, the system dynamically integrates knowledge from external sources, allowing for incremental updates and corrections. The resulting NSKG can autonomously learn and evolve, addressing the limitations of static knowledge graphs commonly used in intelligent applications. The key contributions lie in the automated adaptation process and the ability to represent and reason with evolving knowledge. We present a formal model of the system and outline the key components, focusing on the learning process and the dynamic evolution strategy. The system's performance is assessed through a simulated environment, demonstrating its ability to maintain knowledge consistency and accuracy as new information emerges. The overall goal is to create a robust and intelligent knowledge representation system suitable for real-world applications requiring dynamic reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement Learning (RL) has demonstrated remarkable success in various domains, but its application is significantly hindered by the problem of sparse rewards. In environments where rewards are infrequent or delayed, traditional RL algorithms struggle to learn effective policies. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this challenge. We introduce a framework where the environment is represented as a graph, and a GNN learns a rich state representation capturing complex relationships between elements. This representation enables the RL agent to better explore and exploit sparse reward signals, ultimately improving learning efficiency and performance. The core of our approach lies in the ability of GNNs to aggregate information from neighboring nodes, effectively encoding contextual dependencies that are crucial for navigating sparse reward landscapes. We demonstrate the effectiveness of this approach through a theoretical analysis and outline the key components of the system, focusing on the integration of GNNs with standard RL algorithms. The goal is to provide a robust and scalable solution for RL problems characterized by sparse rewards.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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