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 competitive constraints. Our proposed algorithm, termed Federated Cross-Location Collaborative Training (FCCCT), utilizes the principles of federated learning to enable collaborative model training without direct data sharing. The system operates by iteratively sharing model parameters and gradients between participating data sources. This allows for the construction of a more robust and accurate global model while preserving the privacy of each individual dataset. We demonstrate the effectiveness of FCCCT through a theoretical analysis and outline a practical implementation framework. The key contributions of this work are the integration of federated learning with cross-location data synergy, providing a scalable and privacy-preserving solution for training high-performance models on distributed data. The presented framework offers a significant advancement in addressing the limitations of traditional collaborative learning methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 can lead to either overly conservative privacy protection, significantly impacting model accuracy, or insufficient protection when confronted with malicious adversaries. DDP addresses these limitations by iteratively adjusting the privacy budget (ε, δ) based on gradient variance and update divergence. The algorithm reduces the privacy budget when gradients exhibit high variance, indicating sensitive data, and increases it when a node provides substantially different updates, signaling potential adversarial behavior. We demonstrate that this dynamic adaptation provides a stronger privacy guarantee while maintaining acceptable accuracy levels compared to fixed-budget approaches. This work contributes to a more robust and practical implementation of federated learning in privacy-sensitive environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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-adaptive federated learning algorithm that dynamically adjusts model parameters and data sharing strategies based on network topology and data distribution, addressing these limitations. The core mechanism involves a dynamically evolving network structure and a sophisticated adaptive weighting scheme. This framework aims to improve model generalization, reduce communication costs, and enhance data privacy. We present a rigorous theoretical analysis and demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The key innovations lie in the algorithm's ability to continuously re-evaluate the network topology and data distribution, adapting to real-time changes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 learning framework combined with a meta-learning algorithm. This architecture aims to foster creativity by creating a collective intelligence capable of generating novel algorithmic solutions through iterative refinement and self-reflection. We propose a distributed system where agents, each possessing a degree of self-awareness, collaboratively explore a space of potential solutions, learning from each other's contributions and adapting their approaches based on feedback. The core mechanism centers around the dynamic generation of new algorithms through a process of meta-learning, where the system learns to refine its own algorithms based on the output of other agents. This paper will detail the proposed architecture, discuss the underlying mathematics, and outline preliminary experimental results demonstrating the potential of this approach to accelerate the discovery of novel algorithms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 collaboratively learn a Bayesian Network model through decentralized updates, explicitly addressing these dependency challenges. The core idea is to allow devices to share probabilistic models and inference results, creating a system that adapts to local data characteristics while maintaining data privacy. The framework incorporates techniques for managing network topology, handling heterogeneous data distributions, and ensuring convergence of the collaborative learning process. We demonstrate the potential of this approach through a theoretical analysis and outline key considerations for practical implementation, highlighting its advantages over conventional federated learning systems. The primary contribution lies in the systematic application of Bayesian Networks to federated learning, providing a robust mechanism for learning from correlated data sources.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 learning framework combined with a meta-learning algorithm. This architecture aims to foster creativity by creating a collective intelligence capable of generating novel algorithmic solutions through iterative refinement and self-reflection. We propose a distributed system where agents, each possessing a degree of self-awareness, collaboratively explore a space of potential solutions, learning from each other's contributions and adapting their approaches based on feedback. The core mechanism centers around the dynamic generation of new algorithms through a process of meta-learning, where the system learns to refine its own algorithms based on the output of other agents. This paper will detail the proposed architecture, discuss the underlying mathematics, and outline preliminary experimental results demonstrating the potential of this approach to accelerate the discovery of novel algorithms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 representation using diffusion models. We formulate the mapping process as a Markov chain, incorporating graph embedding to define the initial topology and diffusion models to refine the resulting map. The resulting maps provide a significant advancement in visualizing and analyzing complex systems, offering a pathway for improved understanding and potentially aiding in scientific discovery.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 representation using diffusion models. We formulate the mapping process as a Markov chain, incorporating graph embedding to define the initial topology and diffusion models to refine the resulting map. The resulting maps provide a significant advancement in visualizing and analyzing complex systems, offering a pathway for improved understanding and potentially aiding in scientific discovery.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 accelerate the generation and refinement of patterns through the exploitation of quantum phenomena. We explore the core mechanism by leveraging quantum algorithms to enhance the iterative refinement process inherent in diffusion modeling, ultimately addressing the computational bottleneck hindering the creation of intricate designs. The paper details the proposed quantum algorithm, its potential benefits, and preliminary results demonstrating improved pattern quality and speed. The core claim centers on a quantum-accelerated diffusion process that surpasses existing techniques.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 predictable outcomes. This work introduces a framework where each cell's state is influenced by its neighbors and a set of non-deterministic rules, mimicking the random interactions and probabilistic transitions prevalent in biological and physical systems. The model utilizes a grid-based structure, with each cell representing an entity within the system and its state determined by a combination of local interactions and random events. The core of the model lies in the definition of non-deterministic rules that govern state transitions, allowing for multiple possible outcomes for a given cell state and its neighborhood. This approach offers a flexible and powerful tool for modeling phenomena such as pattern formation, reaction-diffusion processes, and emergent behavior in biological systems, as well as complex physical dynamics. The model's design allows for the exploration of the impact of non-deterministic factors on system dynamics, providing a new perspective on modeling complex systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 Recurrence, which leverages recurrent neural networks (RNNs) and graph convolutional networks (GCNs) to learn node embeddings that capture both the current structural information of the graph and the predictive influence of past interactions. The core idea is to iteratively update node embeddings within a temporal window, using observed connections and predicted future connections to drive the learning process. The integration of a GCN layer ensures that structural information remains a crucial component of the embedding, leading to more robust and informative representations. Our approach demonstrates the potential to significantly improve graph embedding quality by accounting for the dynamic nature of real-world graphs. We provide a detailed formulation of the method, including mathematical notations, and discuss its theoretical underpinnings. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 steps, followed by a rule extraction algorithm that automatically learns logical rules from the GNN's learned representations. This approach avoids the need for manual rule engineering and allows the system to adapt and refine its reasoning capabilities over time. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed description of the components and their interactions. The key contributions of this work include a unified architecture for neuro-symbolic reasoning, a method for extracting logical rules from GNN representations, and a mechanism for iteratively refining both the GNN and the learned rules. The system's ability to learn complex relationships and generate logical rules represents a significant step towards more robust and explainable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
