This research investigates the potential role of quantum entanglement in biological information processing. We explore the feasibility of simulating quantum effects within biological molecules and examining the influence of entanglement on fundamental processes such as DNA sequence recognition and protein folding. The core claim centers on demonstrating how entanglement could provide a mechanism for enhanced computational capabilities within biological systems. This work contributes to the emerging field of quantum biology by proposing a novel framework for understanding biological phenomena through the lens of quantum mechanics, specifically focusing on the emergent properties of entanglement. The theoretical analysis presented here lays the groundwork for future experimental investigations and offers a new perspective on the complexities of life.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predicting protein three-dimensional structures from their amino acid sequences remains a grand challenge in computational biology. Traditional methods have struggled to accurately capture the complex, long-range interactions that govern protein folding. This work proposes a novel approach utilizing Graph Neural Networks (GNNs) to address this challenge through a fragment assembly paradigm. We hypothesize that proteins can be effectively predicted by learning to assemble smaller, interacting fragments based on their local structural characteristics. Our GNN learns to represent individual protein fragments as graphs, capturing their local interactions via node features (amino acid types, residue connections) and edge features (distances, angles). The network then predicts the optimal assembly order of these fragments, ultimately generating a predicted protein structure. This approach avoids the need for explicit conformational search and leverages the powerful representation learning capabilities of GNNs. We demonstrate the feasibility and potential of this approach, outlining a framework for future development and exploration.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term directly into the federated learning objective function to formally represent the differential privacy constraint. This allows for an iterative solution of the resulting Lagrangian problem via distributed optimization, providing a controllable mechanism for balancing privacy and model accuracy. The proposed approach offers a more practical and scalable solution compared to existing methods, particularly in scenarios where precise control over the privacy-accuracy trade-off is desired. The effectiveness of this method is demonstrated through theoretical analysis and conceptual discussion, outlining a pathway for future research and implementation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, known as Byzantine nodes, can inject biased or corrupted models into the training process, compromising the overall model accuracy and potentially introducing harmful biases. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerance (BFT) mechanisms. The core claim is that a BFT-enabled decentralized FL system provides robustness against malicious actors, guaranteeing model convergence even when some nodes are compromised. We outline the system architecture, detailing the BFT protocol integration, aggregation strategies, and communication protocols. The proposed system leverages a verifiable distributed consensus mechanism, allowing for the detection and mitigation of Byzantine behavior. The theoretical analysis demonstrates the system's resilience to arbitrary Byzantine failures and provides a framework for quantifying the impact of malicious participation. This work represents a significant advancement in securing FL deployments, fostering trust and reliability in collaborative learning environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations