108 papers indexed here
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This paper presents a novel approach to collaborative model training leveraging federated learning, specifically designed for scenarios involving geographically dispersed data sources. The core challenge addressed is the 'data island' problem, where valuable data remains siloed due to logistical, regulatory, or competi...
This paper presents a novel approach to differential privacy in federated learning, termed Dynamic Differential Privacy (DDP). The core idea is to adapt the privacy budget dynamically, responding to the varying sensitivity of local data. Traditional federated learning frequently utilizes a static privacy budget, which...
This paper investigates the challenges and opportunities presented by federated learning in dynamic environments. Traditional federated learning approaches often rely on fixed network topology and data distribution, leading to suboptimal model performance and inefficient data utilization. We propose a novel self-adapti...
The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...
This paper presents a novel approach to distributed learning leveraging Federated Bayesian Networks (FBNs). Traditional federated learning methods struggle with capturing complex, non-independent data distributions, often leading to suboptimal global models. We introduce a framework where multiple devices collaborative...
The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...
This paper introduces a novel method for generating detailed topological maps of complex systems, leveraging graph embedding and diffusion models. Traditional topological analysis often struggles with capturing fine-grained details, and this approach aims to overcome this limitation by iteratively refining a graph repr...
This paper introduces a novel method for generating detailed topological maps of complex systems, leveraging graph embedding and diffusion models. Traditional topological analysis often struggles with capturing fine-grained details, and this approach aims to overcome this limitation by iteratively refining a graph repr...
Quantum-enhanced diffusion modeling represents a paradigm shift in pattern generation, offering the potential for significantly faster and more precise synthesis of complex, realistic patterns compared to classical methods. This research investigates the feasibility of implementing a novel quantum algorithm designed to...
This paper proposes a novel computational model based on cellular automata (CA) that incorporates non-deterministic elements to better represent and simulate complex systems exhibiting inherently stochastic behavior. Traditional CA models operate under deterministic rules, limiting their applicability to systems with p...
This paper introduces a novel approach to graph embedding that explicitly incorporates temporal dynamics. Traditional graph embedding techniques typically treat graphs as static structures, neglecting the evolving relationships between nodes over time. We propose a method, Temporal Graph Embedding via Predictive Recurr...
This paper presents a novel framework for neuro-symbolic reasoning, addressing the longstanding challenge of integrating neural networks and symbolic reasoning. The core idea is to leverage the strengths of both approaches by employing a Graph Neural Network (GNN) to represent knowledge and perform initial reasoning st...
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