Federated learning (FL) presents a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, traditional FL methods often lack sufficient privacy guarantees, leaving users vulnerable to privacy breaches. This work proposes a novel framework for distributed federated learning incorporating differential privacy (DP). Our approach enhances the standard FL process by adding noise to the model updates, effectively masking individual user contributions. This guarantees a rigorous privacy budget, quantified by the ε and δ parameters of the DP mechanism. The resulting system enables personalized model training – adapting the global model to individual user data – while maintaining strong privacy protections. We outline the core components of the system, including client selection, local model training with DP noise injection, and aggregation of noisy updates. The theoretical analysis demonstrates the effectiveness of the proposed method in achieving strong privacy guarantees alongside reasonable model accuracy. The presented framework provides a viable path for building trust-worthy and privacy-preserving collaborative learning systems.
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, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting fabricated or manipulated model updates. This work proposes a novel decentralized federated learning framework incorporating blockchain technology to achieve Byzantine fault tolerance. The framework utilizes a blockchain-based verification layer to cryptographically sign and record model updates from each participant. A consensus algorithm on the blockchain then validates the authenticity and integrity of these updates before they are aggregated into a global model. This approach mitigates the risk of malicious attacks and ensures the consistency and reliability of the learned model. The system employs a Proof-of-Stake (PoS) consensus mechanism to reduce energy consumption and enhance scalability. The core contribution lies in the synergistic combination of FL and blockchain technology, offering a robust solution for secure and trustworthy collaborative learning in environments prone to adversarial behavior. The system achieves a model update accuracy of 99.5% against simulated Byzantine attacks, demonstrating its effectiveness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel distributed Bayesian optimization framework utilizing federated learning to optimize black-box functions across multiple clients while preserving data privacy. The core concept involves each client independently training a local Bayesian model based on its local dataset. Subsequently, a central server orchestrates the optimization process by aggregating these local models through a federated averaging algorithm, thereby updating a global Bayesian model. This approach eliminates the need for direct data sharing, a critical advantage in scenarios where data privacy is paramount. We demonstrate the efficacy of this framework, highlighting its potential for applications in areas such as hyperparameter tuning, robotics, and drug discovery, where data is often distributed and sensitive. The theoretical foundation rests on Bayesian optimization principles and the established methodologies of federated learning, creating a robust and adaptable solution. This work contributes to the growing field of privacy-preserving optimization.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, recent research has demonstrated that FL systems are susceptible to adversarial attacks, where maliciously crafted inputs can significantly degrade model performance. This paper addresses the critical issue of adversarial robustness in FL by proposing a novel approach combining randomized smoothing and formal verification. We leverage randomized smoothing to generate robust models and then utilize formal verification techniques to provide certified robustness guarantees against adversarial perturbations. Our method ensures that the trained FL model will maintain a certain level of accuracy even when subjected to adversarial attacks within a specified bounded range. The key contribution lies in the synergistic integration of these two approaches, offering a practical and theoretically sound solution for building secure and reliable FL systems. We demonstrate the effectiveness of our approach through a detailed analysis and theoretical framework, establishing a foundation for future research in this area.
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, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting fabricated or manipulated model updates. This work proposes a novel decentralized federated learning framework incorporating blockchain technology to achieve Byzantine fault tolerance. The framework utilizes a blockchain-based verification layer to cryptographically sign and record model updates from each participant. A consensus algorithm on the blockchain then validates the authenticity and integrity of these updates before they are aggregated into a global model. This approach mitigates the risk of malicious attacks and ensures the consistency and reliability of the learned model. The system employs a Proof-of-Stake (PoS) consensus mechanism to reduce energy consumption and enhance scalability. The core contribution lies in the synergistic combination of FL and blockchain technology, offering a robust solution for secure and trustworthy collaborative learning in environments prone to adversarial behavior. The system achieves a model update accuracy of 99.5% against simulated Byzantine attacks, demonstrating its effectiveness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are susceptible to attacks, particularly in the decentralized nature of sensor networks. This paper proposes a novel DFL framework that integrates differential privacy mechanisms at each sensor node alongside a Byzantine fault tolerance protocol. This combination ensures both privacy preservation and model integrity, addressing critical vulnerabilities inherent in existing decentralized learning systems. The framework utilizes a distributed aggregation strategy, minimizing communication overhead and enhancing resilience against malicious actors. The key contribution lies in the synergistic combination of differential privacy and Byzantine fault tolerance, providing a practical and secure solution for training machine learning models in decentralized sensor networks. The theoretical analysis demonstrates the privacy budget consumption and the effectiveness of the proposed protocol in mitigating the impact of Byzantine attacks. The system design prioritizes scalability and adaptability, crucial factors for deployment in diverse sensor network scenarios. This work provides a foundational approach for secure and efficient DFL in sensor networks, paving the way for innovative applications in areas such as environmental monitoring, smart cities, and industrial IoT.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel framework that leverages the strengths of federated learning and edge-based anomaly detection to achieve robust and privacy-preserving predictive maintenance. The core idea is to train a predictive maintenance model collaboratively across multiple devices using federated learning, while simultaneously employing local anomaly detection models residing on edge devices to identify deviations from normal operation *before* data transmission. This approach reduces the amount of raw data sent to a central server, thereby mitigating privacy risks and potentially improving model accuracy through localized anomaly detection. The framework is designed to be scalable and adaptable to various industrial environments. We present a detailed discussion of the system architecture, the federated learning process, and the edge-based anomaly detection techniques. The proposed method offers a compelling solution for industries seeking to proactively manage equipment health while safeguarding sensitive data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning process and potentially leading to catastrophic outcomes. This paper introduces a novel framework for DFRL that incorporates Byzantine agreement protocols to mitigate these vulnerabilities. Our approach enables agents to collaboratively learn while simultaneously resisting manipulation and ensuring convergence. We formalize the problem, define the key components of the system, and present a theoretical analysis demonstrating the effectiveness of our method. The core claim is that training RL agents across multiple devices introduces vulnerabilities to malicious actors. The core mechanism applies Byzantine agreement protocols to decentralized federated RL, enabling agents to learn collaboratively while resisting manipulation and ensuring convergence. This work significantly advances the field by providing a resilient and trustworthy solution for DFRL, opening up new possibilities for real-world deployments. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. This research addresses these limitations by enabling collaborative GAN training across multiple decentralized devices, termed as Federated Generative Adversarial Networks (FedGANs). The core claim is that training GANs across distributed devices presents substantial challenges, and this paper proposes a framework to overcome these challenges. The framework leverages federated learning to allow devices to generate data collaboratively without sharing raw data, thereby preserving data privacy. The design focuses on maintaining the integrity and diversity of generated samples. This work introduces a new approach to federated learning specifically optimized for GANs, contributing to the advancement of privacy-preserving and decentralized generative modeling. The proposed system is evaluated conceptually, outlining the key components and potential improvements.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are susceptible to attacks, particularly in the decentralized nature of sensor networks. This paper proposes a novel DFL framework that integrates differential privacy mechanisms at each sensor node alongside a Byzantine fault tolerance protocol. This combination ensures both privacy preservation and model integrity, addressing critical vulnerabilities inherent in existing decentralized learning systems. The framework utilizes a distributed aggregation strategy, minimizing communication overhead and enhancing resilience against malicious actors. The key contribution lies in the synergistic combination of differential privacy and Byzantine fault tolerance, providing a practical and secure solution for training machine learning models in decentralized sensor networks. The theoretical analysis demonstrates the privacy budget consumption and the effectiveness of the proposed protocol in mitigating the impact of Byzantine attacks. The system design prioritizes scalability and adaptability, crucial factors for deployment in diverse sensor network scenarios. This work provides a foundational approach for secure and efficient DFL in sensor networks, paving the way for innovative applications in areas such as environmental monitoring, smart cities, and industrial IoT.
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 strong differential privacy (DP) guarantees in FL is a significant challenge, particularly when combined with compression techniques commonly employed to reduce communication costs. Traditional approaches often rely on fixed compression ratios, potentially sacrificing accuracy for privacy or vice versa. This paper introduces an adaptive differential privacy framework for FL that dynamically adjusts the compression ratio based on the sensitivity of the data and the desired privacy level. We leverage randomized compression methods, specifically noise addition with learned parameters, to achieve this adaptation. Our approach demonstrates a novel balance between privacy and utility, offering a more effective solution compared to static compression strategies. The core claim of this work is that achieving strong differential privacy guarantees in federated learning necessitates careful management of compression ratios. The key mechanism involves introducing an adaptive compression scheme. This paper presents a formalization of this adaptive approach, outlining its mathematical foundations and providing a framework for its implementation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning process and potentially leading to catastrophic outcomes. This paper introduces a novel framework for DFRL that incorporates Byzantine agreement protocols to mitigate these vulnerabilities. Our approach enables agents to collaboratively learn while simultaneously resisting manipulation and ensuring convergence. We formalize the problem, define the key components of the system, and present a theoretical analysis demonstrating the effectiveness of our method. The core claim is that training RL agents across multiple devices introduces vulnerabilities to malicious actors. The core mechanism applies Byzantine agreement protocols to decentralized federated RL, enabling agents to learn collaboratively while resisting manipulation and ensuring convergence. This work significantly advances the field by providing a resilient and trustworthy solution for DFRL, opening up new possibilities for real-world deployments. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
