153 papers indexed here
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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 confidential...
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 ch...
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 secu...
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,...
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 mode...
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 relationship...
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 mode...
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,...
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 pro...
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 relationship...
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 gr...
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 approac...
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