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
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
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Contemporary pharmacology remains anchored to a population-centric epistemology, wherein the "average patient" derived from randomized controlled trials (RCTs) serves as the gold standard for dosing and therapeutic decision-making. This framework systematically marginalizes individual heterogeneity in drug response, treating deviation from the mean as statistical noise rather than ontologically significant signal. This manuscript presents the **Computational Heterogeneous-Response Pharmacology (CHRP)** framework, a formalized theoretical architecture for individualized drug verification grounded in the philosophy of **embodied pathology**. CHRP advances three core axioms: (1) individual pharmacological heterogeneity constitutes an ontological reality, not merely measurement error; (2) patient-reported outcomes (PROs) possess epistemic parity with objective biomarkers; and (3) individual drug responses are computationally predictable through multi-scale modeling. From these axioms, CHRP derives a **three-level verification closure**: micro-level digital twin calibration with micro-dosing; meso-level N-of-1 adaptive trials with Bayesian response algorithms; and macro-level federated learning regulatory networks that preserve data sovereignty. The framework distinguishes contextual implementation pathways: developed economies may leverage full digital twin infrastructure and electronic health record integration, while developing regions can deploy lightweight federated learning nodes without raw data leakage. CHRP thus claims **global explanatory universality** at the axiomatic level while demanding **contextual adaptation** at the implementation level. **Current Status Declaration:** This manuscript presents a completed theoretical framework with formalized axioms, mechanisms, and falsifiable propositions. Large-scale multi-center N-of-1 clinical trials to empirically validate its core hypotheses are pending. CHRP is offered as a candidate paradigm for global academic discussion, falsification, and iterative refinement. **Disclaimer:** This theoretical framework does not constitute clinical guidance. All therapeutic decisions must comply with local regulatory requirements and established clinical standards. 当代药理学仍深植于一种以群体为中心的认识论:随机对照试验(RCT)所推导出的"平均患者",是剂量确定与治疗决策的黄金标准。这一框架系统性地边缘化了药物反应的个体异质性,将偏离均值的现象视为统计噪声,而非具有本体论意义的信号。 本文提出**计算异质性响应药理学验证理论(CHRP)**,一个基于**具身病理学**哲学思想的、用于个体用药验证的形式化理论架构。CHRP 提出三条核心公理:(1)个体药理学异质性构成本体论层面的实在,而非单纯的测量误差;(2)患者报告结局(PROs)与客观生物标志物具有同等认识论地位;(3)个体药物反应可通过多尺度建模进行计算预测。基于这三条公理,CHRP 推导出**三级验证闭合机制**:微观层面的数字孪生校准与微剂量探测;中观层面的 N-of-1 自适应试验与贝叶斯响应算法;以及宏观层面的联邦学习监管网络,在保护数据主权的前提下实现跨机构知识聚合。 该框架区分了情境化的实施路径:发达经济体可利用完整的数字孪生基础设施与电子健康档案整合;发展中地区则可部署轻量级联邦学习节点,无需原始数据泄露即可参与全球网络。因此,CHRP 在公理层面主张**全球普适解释力**,在实施层面要求**情境化适配**。 **当前状态声明:** 本文呈现的是一个理论框架已完成的文稿,包含形式化公理、机制设计与可证伪命题。验证其核心假设的大规模多中心 N-of-1 临床试验尚待开展。CHRP 作为候选范式,供全球学术界讨论、证伪与迭代完善。 **免责声明:** 本理论框架不构成临床指导。所有治疗决策须遵循当地监管要求与既定临床标准。
Yi He· Zenodo (CERN European Organi...· 0 citations
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Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This paper proposes a novel framework for distributed federated learning with Byzantine fault tolerance, leveraging the power of homomorphic encryption. The system encrypts model updates using homomorphic encryption, allowing the central server to perform aggregation computations directly on the encrypted data. This approach eliminates the need for decryption, thereby safeguarding client data and providing robust protection against Byzantine attacks. The core claim of this work is that the combination of FL with homomorphic encryption provides a fundamentally secure and resilient solution for distributed model training. We detail the system architecture, the encryption and aggregation protocols, and analyze the security and performance implications. The proposed method significantly enhances the robustness of FL against malicious participants, offering a practical solution for privacy-sensitive applications.
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) 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
This repository contains the dataset associated with the manuscript entitled “Histogram-Optimized CKKS Encrypted Federated XGBoost for Privacy-Preserving Healthcare Analytics.” The experiments use two datasets: A fully reproducible synthetic EHR dataset. The publicly available UCI Heart Disease dataset ( Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R. (1989). Heart Disease [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52P4X.) The synthetic dataset is not intended to represent a real patient cohort. It is provided as a controlled benchmark for evaluating the proposed encrypted federated-learning architecture under reproducible non-IID conditions.
Vijayalakshmi· Zenodo (CERN European Organi...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
