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 proposes a novel approach to network anomaly detection leveraging the strengths of federated learning and graph neural networks (GNNs). Traditional centralized anomaly detection systems suffer from single points of failure and privacy concerns. Our decentralized system addresses these limitations by enabling each network device to independently train a GNN model using federated learning. Each device learns a local representation of its network traffic data, and anomalies are detected by comparing the device's local representation with the global model. The use of GNNs allows for the modeling of complex network topologies and relationships between devices, improving the accuracy of anomaly detection. The federated learning component ensures privacy preservation and reduces the need to transmit sensitive data to a central server. This approach offers a robust and scalable solution for real-time anomaly detection in dynamic network environments. The key contribution lies in the synergistic combination of these techniques to create a resilient and privacy-conscious system.
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
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data without direct data sharing. However, the traditional approach of using static homomorphic encryption (HE) schemes for FL introduces significant overheads, particularly when combined with the iterative nature of the training process. This paper proposes a novel adaptive homomorphic encryption scheme designed specifically for federated learning. The core idea revolves around dynamically adjusting the ciphertext structure of the HE scheme during training, optimizing for computational efficiency and security. We introduce a system where the encryption key evolves based on the data distribution and the learning algorithm employed. This adaptive approach mitigates the inefficiencies of static HE schemes and represents a significant step towards realizing the full potential of HE in FL. The proposed scheme is theoretically sound and provides a framework for future research in this area.
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
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent trust relationships within FL systems are complex and often overlooked. This paper presents a formal model for analyzing trust dynamics in FL, incorporating uncertainties in data and model updates. We leverage game theory and Bayesian networks to represent the interactions between participants, considering factors such as data quality, model accuracy, and communication security. The model allows us to formally quantify trust levels and explore strategies for building robust and reliable FL systems. The key contributions of this work lie in providing a rigorous mathematical framework for understanding trust in FL, which can be used to design mechanisms for incentivizing participation, mitigating malicious behavior, and ultimately, enhancing the overall performance and security of FL systems. The model incorporates probabilistic elements represented through conditional probability tables and utility functions, allowing for a nuanced assessment of trust based on various contributing factors. ---
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
This paper proposes a novel approach to distributed learning that leverages the principles of Federated Byzantine Fault Tolerance (FBT) to achieve robustness and scalability against malicious actors. Traditional federated learning systems are vulnerable to attacks where adversaries can manipulate the global model by injecting poisoned updates. Our method integrates FBT protocols, originally designed for blockchain consensus, into the distributed learning framework. This allows nodes to reach agreement on model updates even when some nodes are compromised and actively attempting to disrupt the learning process. We demonstrate that this combination provides a significant improvement in security and reliability compared to standard federated learning, offering a pathway to secure and trustworthy distributed model training. The core claim of this work is that robust and scalable distributed learning in the presence of malicious actors is a significant challenge, and our proposed solution directly addresses this issue.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidentiality. Federated learning offers a decentralized alternative, training models across distributed devices without direct data sharing. However, inherent vulnerabilities remain due to the model updates themselves potentially revealing sensitive information. This work addresses this challenge by introducing a framework that incorporates differential privacy mechanisms directly into the federated learning process. We detail a proposed algorithm that adds calibrated noise to model updates, ensuring that the influence of any single patient's data on the global model is limited. The core contribution is a novel approach to balancing the privacy guarantees of DP with the utility requirements of FL, particularly within the sensitive domain of healthcare. The results demonstrate the feasibility of achieving privacy-preserving personalized healthcare models and highlight potential avenues for future research. Mathematical notation and formulas are presented in plain text for seamless copy-pasting.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach still exposes participants to potential privacy risks. Our method leverages homomorphic encryption to enable computations on encrypted data, mitigating the risk of direct data exposure. Subsequently, we apply differential privacy techniques to the aggregated model updates, providing a rigorous guarantee against individual data disclosure. The core claim is that this combined strategy offers a robust solution for preserving data privacy in federated learning settings. We outline the key mechanisms involved, demonstrating how homomorphic encryption is used to perform computations on encrypted data and how differential privacy is applied to the resulting aggregated model updates. The proposed framework addresses the unique challenges posed by federated learning, offering a significant improvement in privacy protection.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) has emerged as a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of current FL protocols to Byzantine attacks – where malicious nodes inject false information to corrupt the global model – poses a significant threat to data privacy and model accuracy. This paper proposes a novel decentralized federated learning protocol leveraging a Byzantine-resilient graph protocol. Our approach utilizes a distributed graph structure where nodes communicate and learn through this graph, incorporating mechanisms to detect and mitigate the influence of malicious actors. The core claim is that current FL protocols are susceptible to Byzantine attacks. The proposed mechanism introduces a robust decentralized system capable of handling adversarial behavior, thus improving the overall resilience of the learning process. We introduce a framework for designing and implementing such protocols, focusing on graph construction, message authentication, and consensus mechanisms. The resulting system offers enhanced data privacy and model accuracy compared to traditional, centralized FL methods.
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, this approach inherently exposes sensitive user data, raising significant privacy concerns. This paper proposes a novel framework for distributed differential privacy (DDP) within a secure multiparty computation (SMC) environment for FL. The core idea is to utilize SMC to guarantee differential privacy while minimizing communication overhead and computational complexity. Our protocol constructs a distributed computation where each client contributes to a shared global model update, shielded by the privacy guarantees of differential privacy. The proposed system aims to achieve a strong privacy-utility trade-off, addressing a critical gap in existing FL solutions. We formally define the system, outline the protocol, and analyze its privacy and computational characteristics. The key contributions lie in the synergistic combination of SMC and differential privacy within a federated setting, providing a robust solution for privacy-preserving machine learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
