This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to decentralized federated reinforcement learning (DFRL) that tackles the critical challenge of multi-agent credit assignment. Traditional multi-agent reinforcement learning (MARL) struggles to scale effectively in decentralized settings due to the difficulty in determining which agents are responsible for the overall reward. Our method introduces a decentralized credit assignment algorithm leveraging Shapley values, providing a fair and efficient mechanism for distributing rewards among agents. Combined with a communication protocol, this architecture enables scalable and robust learning across multiple agents operating independently. The core claim is that innovative credit assignment solutions are necessary to scale MARL in decentralized environments. This work offers a foundational framework for developing more practical and effective DFRL systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, achieving robust privacy guarantees alongside high model accuracy remains a significant challenge. This paper introduces a novel approach to decentralized federated learning leveraging Lagrangian relaxation and differential privacy. Our method allows for a more nuanced and adaptive trade-off between privacy and accuracy by optimizing a Lagrangian function at each participating node. This decentralized optimization process enables a flexible solution to the inherent tension between privacy preservation and model performance. We demonstrate the effectiveness of this approach through a theoretical analysis and outline a potential implementation strategy. The key innovation lies in the localized optimization of the Lagrangian, mitigating the communication bottlenecks often encountered in centralized FL and offering a pathway towards truly decentralized privacy-preserving learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) has emerged as a promising approach for training machine learning models across decentralized devices while preserving data privacy. However, a significant limitation of traditional FL is the static nature of model selection. Typically, all clients are trained with the same base model, regardless of the heterogeneity in their local data distributions. This static approach often leads to suboptimal performance, particularly in scenarios with diverse client datasets. This paper proposes a novel meta-learning framework to address this limitation. The framework learns a central model that dynamically selects the most appropriate base model for each client based on its local data characteristics. This adaptive model selection process aims to improve the overall system performance and efficiency of federated learning. The key contributions are the introduction of adaptability into the model selection stage and the development of a meta-learning approach for achieving this adaptation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated learning. Each node maintains its own Bayesian network and updates its parameters based on probabilistic information received from its neighbors. This approach avoids the need for centralized data aggregation, addressing key challenges associated with privacy and scalability in traditional Bayesian network learning. The algorithm iteratively refines both the network structure and its parameters, leading to a more accurate and robust global model. Mathematical formulations are presented to detail the update rules and convergence properties of the FBN algorithm. The key contribution lies in establishing a framework for distributed Bayesian network learning, particularly well-suited for scenarios with heterogeneous data and limited communication bandwidth. This work lays the foundation for applying FBNs to diverse applications, including healthcare, smart cities, and anomaly detection.
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 systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these concerns by integrating data provenance tracking, integrity verification, and differential privacy mechanisms. The system leverages blockchain technology to create an immutable record of model updates and data contributions, ensuring transparency and accountability. Simultaneously, differential privacy techniques are applied during the training process to protect the privacy of individual data contributors. This combined approach significantly enhances the security and trustworthiness of FL systems, enabling secure and collaborative model training across diverse data sources. The proposed system utilizes cryptographic hashing and Merkle trees to guarantee data integrity and employs noise injection strategies within differential privacy mechanisms to protect user data. The core contribution is a secure and verifiable FL framework with enhanced privacy guarantees.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) has emerged as a promising approach for training machine learning models across decentralized devices while preserving data privacy. However, a significant limitation of traditional FL is the static nature of model selection. Typically, all clients are trained with the same base model, regardless of the heterogeneity in their local data distributions. This static approach often leads to suboptimal performance, particularly in scenarios with diverse client datasets. This paper proposes a novel meta-learning framework to address this limitation. The framework learns a central model that dynamically selects the most appropriate base model for each client based on its local data characteristics. This adaptive model selection process aims to improve the overall system performance and efficiency of federated learning. The key contributions are the introduction of adaptability into the model selection stage and the development of a meta-learning approach for achieving this adaptation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Traditional Federated Learning (FL) solutions, while offering a degree of data privacy, still rely on centralized aggregation, a point of vulnerability. Our proposed DFLDP framework addresses this limitation by adopting a decentralized architecture where individual researchers maintain complete control over their datasets. Crucially, we integrate differential privacy mechanisms directly into the aggregation process, adding a quantifiable layer of protection against data leakage. This ensures that the learned model benefits from the collective knowledge of multiple researchers without revealing individual data contributions. The system utilizes a gossip-based communication protocol for model updates, minimizing communication overhead. We formally define the mathematical framework, outlining the key components and their interactions. The system's performance is evaluated in a simulated environment, demonstrating the effectiveness of the DFLDP approach in achieving accurate models while upholding stringent privacy guarantees. The core claim of this work is that sharing raw scientific data for federated learning is often prohibited due to privacy concerns and intellectual property restrictions. The core mechanism implemented is the realization of a federated learning system that utilizes differential privacy to protect data during aggregation, while also employing a decentralized architecture where individual researchers retain control over their data. This new approach combines federated learning with differential privacy and decentralization, enabling collaborative scientific discovery without compromising data privacy or intellectual property rights.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately compromising the global model's integrity. This paper proposes a novel decentralized federated learning system incorporating Byzantine fault tolerance (BFT) achieved through a blockchain-based verification layer. The system leverages blockchain technology to cryptographically verify model updates from each participant before they are aggregated into the global model. This ensures data integrity and enables the detection and mitigation of Byzantine attacks. The core claim is that existing FL systems are vulnerable to these attacks. The proposed mechanism implements a decentralized FL system utilizing a blockchain-based verification layer. This represents a new approach to robust FL training. The system utilizes the following key components: participant nodes, a blockchain network, and a global model aggregator. The blockchain network is composed of multiple nodes that maintain a ledger of all model updates. The global model aggregator utilizes the blockchain network to verify model updates before aggregating them into the global model. The proposed system enhances the security and robustness of FL training by providing a tamper-proof audit trail and enabling the detection of malicious participants.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) is a machine learning technique that enables training models on decentralized data sources while preserving data privacy. However, traditional FL approaches often rely on global model updates, which can be vulnerable to adversarial attacks and suboptimal performance on heterogeneous data. This paper proposes a novel framework called Adaptive Parameter Adjustment Federated Learning (APAFL) that dynamically adjusts model parameters based on local data characteristics, enhancing model robustness and privacy. We present a mechanism for adaptive parameter adjustment using a weighted aggregation of local model updates, mitigating the impact of noisy data and promoting better generalization. The proposed method demonstrates significant improvements in model accuracy and privacy compared to existing FL techniques.
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 systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these concerns by integrating data provenance tracking, integrity verification, and differential privacy mechanisms. The system leverages blockchain technology to create an immutable record of model updates and data contributions, ensuring transparency and accountability. Simultaneously, differential privacy techniques are applied during the training process to protect the privacy of individual data contributors. This combined approach significantly enhances the security and trustworthiness of FL systems, enabling secure and collaborative model training across diverse data sources. The proposed system utilizes cryptographic hashing and Merkle trees to guarantee data integrity and employs noise injection strategies within differential privacy mechanisms to protect user data. The core contribution is a secure and verifiable FL framework with enhanced privacy guarantees.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
