Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Walk Based Federated Learning (QWFL), that addresses these limitations by integrating the principles of quantum walks. We leverage quantum superposition and interference to create a secure and efficient model aggregation process. The core idea is to represent model updates as quantum walks on a graph representing the distributed devices, where the walk's evolution inherently obfuscates information. This approach significantly reduces the risk of privacy breaches while concurrently minimizing communication overhead. The theoretical analysis demonstrates the potential of QWFL to achieve better privacy guarantees and communication efficiency compared to conventional FL methods. We present a formal mathematical model of the QWFL system and analyze its key properties, including the walk's convergence rate and the level of privacy protection afforded by the quantum walk process. The design aims for practical implementation by considering the challenges of integrating quantum computing technologies into existing FL architectures. The results indicate that QWFL provides a viable pathway towards robust and scalable distributed learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel system utilizing distributed reinforcement learning (DRL) across a network of autonomous vehicles. The core claim is that a decentralized system, enabling collaborative learning and adaptation, can achieve improved performance and robustness compared to centralized methods. The system employs vehicles to share observations and jointly learn navigation policies. The key mechanisms involve the distributed training of policies using techniques such as asynchronous difference methods and federated learning. The system addresses the problem of non-stationarity inherent in multi-agent systems by employing adaptive learning rates and robust policy aggregation strategies. We discuss the theoretical framework, highlighting key concepts like policy synchronization, convergence analysis, and the impact of communication constraints. Furthermore, we present a conceptual model of the system, outlining the interactions between vehicles and the learning process. The potential benefits of DML for autonomous vehicle control, including enhanced safety, increased scalability, and improved adaptability to dynamic environments, are explored. The research contributes to the growing field of DML by providing a foundational design and theoretical underpinnings for a robust and scalable autonomous driving system.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized datasets without directly exchanging the data itself. However, integrating differential privacy (DP) into FL introduces significant accuracy trade-offs. This work proposes a novel approach, "Entangled Noise," to mitigate these trade-offs. Entangled noise leverages a specialized noise injection strategy that minimizes communication overhead while maintaining a higher level of privacy guarantees. We demonstrate that standard DP methods often overly restrict model updates, leading to substantial accuracy degradation. Our entangled noise technique intelligently combines noise injections across model parameters, effectively reducing the overall noise variance and preserving model performance. We present a theoretical framework analyzing the privacy-utility trade-off and provide empirical evidence showcasing the superior performance of entangled noise compared to traditional DP methods in federated learning scenarios. The key innovation lies in the dynamic adjustment of noise parameters based on parameter sensitivity, resulting in a more efficient and accurate DP-FL system. This work contributes a practical and theoretically sound method for achieving stronger privacy guarantees in federated learning without sacrificing model accuracy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel federated learning optimization algorithm that integrates differential privacy mechanisms to safeguard user data privacy while simultaneously enhancing learning efficiency. Federated learning, while promising for distributed model training, presents significant privacy challenges. Our proposed algorithm addresses this by introducing a privacy-preserving layer directly within the gradient update process, mitigating the risk of sensitive data leakage. We demonstrate through theoretical analysis and a detailed algorithm description that our approach achieves a robust balance between privacy protection and model accuracy, offering a viable solution for privacy-sensitive federated learning applications. The core claim of this work is to propose an optimization algorithm based on differential privacy for federated learning, protecting user data privacy while improving learning efficiency. The core mechanism is introducing a privacy-protecting mechanism in the gradient update process of federated learning to prevent user data leakage.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel framework for secure federated learning leveraging the inherent properties of blockchain technology. The core claim is that utilizing blockchain's distributed consensus mechanism can establish a robust and reliable system for federated learning, effectively safeguarding the privacy of participating entities' data. The proposed mechanism involves recording model updates on a blockchain, employing consensus to guarantee the authenticity and consistency of these updates, and integrating encryption techniques to protect data privacy. This approach addresses the existing security vulnerabilities within traditional federated learning systems, significantly enhancing their overall security posture. The system's architecture and operational principles are detailed, outlining a practical solution for building trustworthy and privacy-preserving federated learning environments. Further, the paper explores potential challenges and future research directions within this domain.
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, existing privacy-preserving techniques frequently rely on approximations and heuristics, often lacking rigorous mathematical justification. This work presents a novel information-theoretic framework for analyzing and controlling information leakage in federated learning. We leverage concepts from source coding and channel capacity to derive tight bounds on the information revealed by local model updates. These bounds provide provable privacy guarantees, offering a significant advancement over existing methods. The core contribution lies in establishing a solid, mathematically grounded foundation for FL privacy, facilitating the design of more robust and efficient privacy-preserving algorithms. We demonstrate the framework's applicability through theoretical analysis and illustrate its potential for optimizing privacy-utility trade-offs within FL systems. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to decentralized knowledge graph construction using Bayesian Networks and Federated Learning. The core idea is to distribute the knowledge graph construction process across a network of nodes, each maintaining a local Bayesian Network. Federated Learning is then employed to iteratively refine these local networks by aggregating anonymized data, ultimately leading to a globally consistent and accurate knowledge graph. This architecture mitigates the risks associated with centralized data silos, enhancing data privacy and improving the robustness of the knowledge representation. The proposed method leverages the probabilistic reasoning capabilities of Bayesian Networks alongside the efficiency of Federated Learning, offering a scalable and adaptive solution for knowledge graph development in distributed environments. The key contributions of this work include the integration of these two powerful techniques and the demonstration of their effectiveness in constructing decentralized knowledge graphs. Specifically, we explore the mathematical foundations underpinning the Bayesian Network representation and the Federated Learning algorithm, highlighting the convergence properties and optimization strategies involved. The resulting knowledge graph reflects a nuanced understanding of the underlying data, capturing relationships and dependencies in a robust and privacy-preserving manner.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning models, particularly in scenarios where models are trained on diverse and potentially biased datasets. Traditional approaches to achieving fairness often rely on centralized data collection and model training, which can be impractical, raise significant privacy concerns, and exacerbate existing biases due to skewed data distributions. This paper proposes a novel framework for achieving algorithmic fairness in distributed learning environments by integrating differential privacy with federated learning. Our approach allows individual clients to contribute data to a global model without revealing their sensitive information, while simultaneously mitigating bias introduced by biased data distributions. We leverage the privacy guarantees of differential privacy to protect client data and use federated learning to aggregate model updates from multiple clients. The core of our method involves carefully tuning the differential privacy parameters and adapting the federated learning algorithm to account for potential biases in the underlying data. The resulting model exhibits improved fairness metrics compared to traditional federated learning approaches. We demonstrate the effectiveness of our framework through a theoretical analysis and a simulation study. The primary contribution of this work is a practical and privacy-preserving method for fostering algorithmic fairness in decentralized learning systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 challenge. Our method utilizes a local perturbation strategy at each client, combined with a global privacy accounting protocol. This approach guarantees differential privacy at the client level, minimizing the risk of individual data disclosure, without incurring a significant drop in model accuracy. We demonstrate that this tailored DDP solution offers a more sophisticated and effective approach compared to existing methods, providing a robust framework for privacy-preserving FL. The key contributions of this paper are the design of the local perturbation strategy and the global privacy accounting protocol, both optimized for the unique constraints of the federated learning setting. This results in a system that balances privacy protection and model utility.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel system utilizing distributed reinforcement learning (DRL) across a network of autonomous vehicles. The core claim is that a decentralized system, enabling collaborative learning and adaptation, can achieve improved performance and robustness compared to centralized methods. The system employs vehicles to share observations and jointly learn navigation policies. The key mechanisms involve the distributed training of policies using techniques such as asynchronous difference methods and federated learning. The system addresses the problem of non-stationarity inherent in multi-agent systems by employing adaptive learning rates and robust policy aggregation strategies. We discuss the theoretical framework, highlighting key concepts like policy synchronization, convergence analysis, and the impact of communication constraints. Furthermore, we present a conceptual model of the system, outlining the interactions between vehicles and the learning process. The potential benefits of DML for autonomous vehicle control, including enhanced safety, increased scalability, and improved adaptability to dynamic environments, are explored. The research contributes to the growing field of DML by providing a foundational design and theoretical underpinnings for a robust and scalable autonomous driving system.
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 the data itself. However, current FL frameworks are susceptible to attacks from malicious participants, known as Byzantine attacks, which can compromise model integrity and potentially inject biases. This paper proposes a novel decentralized federated optimization system leveraging blockchain technology and Byzantine Fault Tolerance (BFT) consensus algorithms to mitigate these vulnerabilities. Our system allows participants to securely contribute model updates and verify their correctness, ensuring a robust and resilient optimization process. The core innovation lies in integrating blockchain's immutability and cryptographic capabilities with BFT to create a system capable of detecting and rejecting malicious updates, thereby safeguarding the global model from manipulation. This approach provides a fundamentally more secure and trustworthy FL environment compared to traditional centralized or even existing decentralized FL methods. We explore the theoretical foundations of this system and outline its key components, focusing on the BFT consensus mechanism and its integration with the FL optimization process. The system's design emphasizes transparency, auditability, and resistance to adversarial attacks, representing a significant advancement in the field of secure and reliable distributed machine learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of federated learning and differential privacy for decentralized anomaly detection in network traffic. Traditional anomaly detection methods often rely on centralized data collection, posing significant privacy risks and hindering scalability. This research proposes a novel framework that leverages the distributed nature of network traffic data by employing federated learning. Each participating device trains a local anomaly detection model on its own data, and these models are then aggregated to create a global model. Crucially, differential privacy is integrated into this process to protect the privacy of individual user data. The proposed approach addresses the challenges of data silos, privacy concerns, and model accuracy simultaneously. The core claim of this work is that combining federated learning with differential privacy provides a viable solution for decentralized anomaly detection. We outline the key mechanisms, including the federated learning system architecture and the implementation of differential privacy, and discuss the potential benefits and challenges of this approach. The resulting anomaly detection model benefits from the collective knowledge of the network traffic data without compromising individual user privacy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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