This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual datasets to the central server, raising significant privacy concerns. Our framework addresses this limitation by employing SMPC protocols, enabling model updates to be aggregated securely without revealing the underlying data. This approach provides a strong privacy guarantee, combining the benefits of federated learning with robust privacy protection. We present a detailed description of the system architecture, the SMPC protocols utilized, and the mathematical formulation underpinning the aggregation process. The core claim of this work is the ability to enable collaborative model training across multiple parties without revealing individual data. The core mechanism relies on implementing a federated learning framework based on secure multi-party computation protocols, where model updates are aggregated securely without exposing the underlying data. This research represents a significant advancement in the field of privacy-preserving machine learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to decentralized federated learning (DFL) that incorporates Byzantine fault tolerance (BFT) using blockchain consensus. Traditional DFL systems are vulnerable to malicious clients injecting poisoned model updates, compromising the overall model accuracy and data integrity. Our system leverages a blockchain network to provide a robust and verifiable mechanism for validating model updates. Each client's update is cryptographically signed and submitted to the blockchain. The blockchain then employs a consensus algorithm to verify the update's validity, ensuring that only legitimate updates are added to the global model. This approach significantly enhances the security and reliability of DFL, particularly in environments with untrusted participants. The core claim is that blockchain-based consensus mechanisms can provide robust BFT for DFL, while the core mechanism involves using a blockchain network to verify and validate model updates. This addresses the security vulnerabilities of traditional DFL by utilizing the inherent properties of blockchain technology. We demonstrate the feasibility and effectiveness of this system through a detailed theoretical analysis and design considerations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel information-theoretic framework for quantifying trust in FL, moving away from traditional subjective trust assumptions. We define a "trust score" based on the mutual information between local model updates and a global consensus model. This score directly measures the contribution of each client's update to the collective knowledge, providing a rigorous metric for assessing model divergence and identifying potential outliers. The core contribution of this paper lies in providing a quantifiable and objective method for evaluating trust in FL, enabling more robust and reliable deployments. We explore the theoretical properties of this mutual information-based trust score and demonstrate its potential for improved outlier detection and model convergence in federated learning scenarios. The framework utilizes concepts from information theory, specifically mutual information, to provide a mathematically grounded approach to assessing the quality and trustworthiness of decentralized model training.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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
