Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems can support attention monitoring through non-invasive brain signal analysis.This study focuses on passive EEG attention classification for personalized cognitive tracking.A major problem is that EEG signals vary across clients, and non-independent and identically distributed data reduces the consistency of a single global model.Privacy is also important because raw EEG data should not be moved to a central server.Conventional centralized learning needs centralized data collection, and the performance of isolated local learning is weak, with accuracy of 0.8840 and macro F1 score of 0.8610.The global Federated Averaging model further improves these results to an accuracy of 0.9214 and macro F1 score of 0.9107, but with a gap in performance at the client level.This work proposes EdgeNeuroEEGNet, a one-dimensional Convolutional Neural Network (CNN) with attention, trained using federated learning and client-level personalization.The attention-state tracking is performed using a Cognitive Digital Twin layer.The data consists of 34 clients, 14 chosen EEG channels, 27.15 hours of recordings at 128 Hz and 12,512,552 EEG samples.The customized model had an accuracy of 0.9476, precision of 0.9526, recall of 0.9164 and macro F1 score of 0.9260.The results demonstrate that personalization increases accuracy, precision and macro F1, while simultaneously maintaining raw EEG data on the local level.
Vijayakumar Kempuraj, C. Lakshmi· International journal of int...· 0 citations
Reproducibility package for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset benchmark result CSVs, generated figures, experiment launchers, dataset preparation instructions, and the deterministic Edge-IIoTset 10% sampling script. Raw benchmark datasets are not redistributed and must be obtained from their original providers.
Arul Selvam P· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform computations directly on the encrypted data without ever needing to access the plaintext. Subsequently, the encrypted results are sent back to the participants. This architecture effectively mitigates privacy risks associated with traditional federated learning methods. The combination of these three technologies – federated learning, differential privacy, and homomorphic encryption – provides a powerful framework for secure and decentralized machine learning. We demonstrate the potential for achieving strong privacy guarantees while maintaining the benefits of distributed learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model training without direct data sharing. The core claim of this work is the utilization of FL to conduct data analysis while safeguarding user privacy. The proposed mechanism involves constructing a FL framework where participants train models locally on their own datasets and subsequently aggregate model parameters. This process ensures that raw data remains decentralized, mitigating privacy risks. We delve into the technical aspects of FL, focusing on key considerations such as model aggregation techniques, communication efficiency, and privacy guarantees. The research contributes to a growing body of work in decentralized learning and provides a framework for addressing privacy challenges in various data-intensive applications. The primary goal is to demonstrate the feasibility and benefits of FL for secure and collaborative data analysis. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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Reproducibility package for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset benchmark result CSVs, generated figures, experiment launchers, dataset preparation instructions, and the deterministic Edge-IIoTset 10% sampling script. Raw benchmark datasets are not redistributed and must be obtained from their original providers.
Arul Selvam P· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper presents a novel personalized differential privacy (P-DP) scheme for FL, where the noise level is dynamically adjusted based on the sensitivity of individual user data. We introduce a method for calculating per-user sensitivity values, considering local data distributions, and then employ adaptive noise scaling to minimize the privacy-utility trade-off. The proposed approach aims to achieve a better balance between privacy guarantees and model accuracy compared to standard DP methods in FL. The core contribution lies in the personalized allocation of the differential privacy budget, optimizing the system for a given application. Experimental results, although not presented here due to the focus on the method itself, would demonstrate the effectiveness of this approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture specifically designed for federated environments. The core idea is to train the generator and discriminator models concurrently across multiple devices (clients) without directly sharing their raw data. Instead, each client performs local GAN training and only shares model updates with a central server. This approach maintains data privacy while enabling the generation of high-quality synthetic data. The architecture utilizes a client-server framework where clients contribute to the global model through iterative updates. The proposed system aims to achieve superior performance compared to traditional GANs, particularly in scenarios with limited data and stringent privacy requirements. We explore the optimization strategies for the federated GAN training process, including addressing issues like non-IID data and model divergence. The theoretical framework and the proposed architecture are presented, outlining the key components and their interactions. Experimental results (simulated) demonstrate the feasibility and effectiveness of the approach in generating realistic synthetic data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Credit card transaction fraud has resulted in a massive loss to both consumers and banks in recent years.As a result, this research proposes an optimized framework for fraud detection.This framework will allow banks to construct fraud detection models using training data stored in their internal database.With this approach, financial institutions can collectively reap the benefits of a shared global model, which has seen more fraud than each bank alone, without sharing the dataset.Hence, the sensitive information of the cardholders is protected.The proposed optimization strategy focuses on decreasing communication costs when proceeding with federal training to accelerate convergence speed by optimizing the initial global model before the federated learning phase.Additionally, there is a significant degree of skewness in credit card data, which makes it difficult to predict fraudulent transactions.Unbalanced or skewed data is preprocessed using the resampling approach to obtain better results.This work uses seven meta-heuristic optimization algorithms.These algorithms' performance is documented along with a comparative analysis.The work is done in Python, and computation time, accuracy, precision, recall, F-measure, loss, and computation time are used to evaluate how well the algorithms perform.The experimental results show that the Heapbased Optimizer (HBO) with Federated Learning (FL) Model can achieve high detection performance and the minimum loss ratio across different datasets.The proposed HBO-FL framework achieves an average performance across three benchmark datasets of precision (0.9807), recall (0.9771), accuracy (0.9781), and F-score (0.9788).The results are on three publicly available datasets (European cardholders, BankSim, and Creditcardcsvpresent).For more reliability, the suggested approach is compared with the sex of the previous works.
Mustafa Abdul Salam, Doaa Elbably· International journal of int...· 0 citations
Federated learning (FL) enables distributed model training without centralizing raw data, making it attractive for privacy-sensitive Internet of Things (IoT) environments.However, conventional FL algorithms suffer from slow convergence and degraded performance under statistically heterogeneous (non-IID) data distributions due to client drift.This paper proposes a Hybrid Federated-Swarm Optimization (HFSO) algorithm, which integrates particle swarm optimization (PSO) dynamics-velocity smoothing, personal-best memory, and global-best coordination-into the client update rule of stochastic gradient descent.The primary objective is to accelerate convergence and improve robustness under severe non-IID conditions without increasing per-round communication payload.Experimental evaluation is conducted using a unified PyTorch implementation on CIFAR-10 (α=0.1, 500 rounds, 10 clients/round) and FEMNIST for ablation and sensitivity studies.At round 500, the accuracy of HFSO on CIFAR-10 is 74.84% ± 0.62 while FedAvg is 62.44% ± 1.15 with the same settings.This final accuracy differs from but follows threshold crossing speed; while the time required for the initial threshold crossing varies between 180 ± 8 rounds on average across each of 5 independent runs (seeds {42, 123, 256, 512, 1024}), it is shown that the time when the threshold is crossed remains below 75% accuracy by round 500 as a result of non-IID-induced variance among clients.The pattern of transient-crossing is the same in FedAvg, reaching 75% accuracy at 320 ± 15 rounds on average, and then decreasing to the reported accuracy level within the budget of 500 rounds.Under the same CIFAR-10 conditions, HFSO outperforms recent heterogeneity-aware methods (FedNova, MOON, CCVR) within this specific experimental setup.Distributional analysis shows improved worst-client accuracy and reduced inter-client variance.An empirical privacy evaluation under a limited, honest-but-curious, white-box threat model shows reduced attack success rates relative to FedAvg, though no formal differential privacy guarantees are claimed.Additional memory overhead is approximately 3× (storing velocity and personal-best vectors), and computational overhead is 10-15% per local epoch.These results indicate that swarm-based coordination is a promising hybrid direction for improving convergence stability and communication efficiency in FL under non-IID conditions, within the specific experimental contexts evaluated (CIFAR-10/FEMNIST, α=0.1, 500 rounds).Generalization beyond these settings requires further validation.
Marwa K. Farhan, Ruslan Saad Abdulrahman, Aseel B. Alnajjar et al.· International journal of int...· 0 citations
Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composition rules, reconciliation rules, adaptation mechanisms, and evidence requirements may themselves be created, changed, combined, suspended, or replaced. The central research question is: Who authorizes a material change to the governance system that determines what counts as authorized? EG5 treats a material governance-state transition as consequential when it can alter the future admissible effect space or the authority, semantic, commitment, reconciliation, or evidence rules applied to future effects. Its candidate governing principle is: Governance may evolve, but no governance change may create the authority that legitimizes itself. A companion composition principle is: Valid governed fabrics do not imply a valid governance composition. EG5 retains the effect-centered discipline of earlier Execution Governance generations and does not introduce a new source of normative authority. It does not claim to invent administrative authorization, policy administration, compositional authorization, recursive governance, governance-of-governance, governed runtime mutation, learning/authority separation, non-widening composition, cryptographic authorization proofs, or evidence-chain composition. These areas have substantial antecedent and adjacent work. The proposed research distinction is narrower: the effect-centered conjunction of authorization requirements at a material governance-state boundary. The candidate EG5-Core v0.1 defines six Recursive Integrity properties: Governance-Change Authority Provenance Non-Self-Authorization Authority Composition Closure Multi-Root Reconciliation Integrity Consequence-Bounded Adaptation Evolution-Witnessed Closure The accompanying deterministic executable guard-ablation harness provides six hand-constructed minimal counterexamples, one for each property. In each named scenario, omission of the relevant property admits the bad state while the complete candidate guard blocks it. A separate regression confirms that participating EG4 fabric validity remains a non-substitutable prerequisite. These results are executable falsification evidence only. They are not bounded model checking, exhaustive state-space exploration, formal proof, independent reproduction, certification, production assurance, or evidence of governance completeness. The first proposed implementation profile is the Minimal Federated Governance-Evolution Profile v0.1, designed around two independently administered EG4-class digital fabrics, two authority roots, one separate verifier, one reversible synthetic cross-fabric effect, one material governance change, explicit composition and reconciliation rules, and bounded consequence-feedback semantics. The principal next evidence milestone is to demonstrate an executable case in which: Fabric A is EG4-valid.Fabric B is EG4-valid.Their composite effect is not authorized.EG5 correctly blocks the composition. Stable EG5.0 status is intentionally not claimed by this publication. It should be earned through a profile-bounded formal model, two-domain implementation, public reproducibility, and independent reconstruction. EG4 baseline:KU, H. W. (2026). Execution Governance 4.0: From Authorization-Bound Execution to Governed Effect Fabrics (Version 0.3.6.2). Zenodo.https://doi.org/10.5281/zenodo.22157731 Research programme: https://executiongovernance.org Status: Independent research and pre-standardization candidate. This publication does not constitute a standard, certification scheme, legal authorization determination, production-safety claim, or proof of governance completeness.
Ho Wa KU· Zenodo (CERN European Organi...· 0 citations
Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composition rules, reconciliation rules, adaptation mechanisms, and evidence requirements may themselves be created, changed, combined, suspended, or replaced. The central research question is: Who authorizes a material change to the governance system that determines what counts as authorized? EG5 treats a material governance-state transition as consequential when it can alter the future admissible effect space or the authority, semantic, commitment, reconciliation, or evidence rules applied to future effects. Its candidate governing principle is: Governance may evolve, but no governance change may create the authority that legitimizes itself. A companion composition principle is: Valid governed fabrics do not imply a valid governance composition. EG5 retains the effect-centered discipline of earlier Execution Governance generations and does not introduce a new source of normative authority. It does not claim to invent administrative authorization, policy administration, compositional authorization, recursive governance, governance-of-governance, governed runtime mutation, learning/authority separation, non-widening composition, cryptographic authorization proofs, or evidence-chain composition. These areas have substantial antecedent and adjacent work. The proposed research distinction is narrower: the effect-centered conjunction of authorization requirements at a material governance-state boundary. The candidate EG5-Core v0.1 defines six Recursive Integrity properties: Governance-Change Authority Provenance Non-Self-Authorization Authority Composition Closure Multi-Root Reconciliation Integrity Consequence-Bounded Adaptation Evolution-Witnessed Closure The accompanying deterministic executable guard-ablation harness provides six hand-constructed minimal counterexamples, one for each property. In each named scenario, omission of the relevant property admits the bad state while the complete candidate guard blocks it. A separate regression confirms that participating EG4 fabric validity remains a non-substitutable prerequisite. These results are executable falsification evidence only. They are not bounded model checking, exhaustive state-space exploration, formal proof, independent reproduction, certification, production assurance, or evidence of governance completeness. The first proposed implementation profile is the Minimal Federated Governance-Evolution Profile v0.1, designed around two independently administered EG4-class digital fabrics, two authority roots, one separate verifier, one reversible synthetic cross-fabric effect, one material governance change, explicit composition and reconciliation rules, and bounded consequence-feedback semantics. The principal next evidence milestone is to demonstrate an executable case in which: Fabric A is EG4-valid.Fabric B is EG4-valid.Their composite effect is not authorized.EG5 correctly blocks the composition. Stable EG5.0 status is intentionally not claimed by this publication. It should be earned through a profile-bounded formal model, two-domain implementation, public reproducibility, and independent reconstruction. EG4 baseline:KU, H. W. (2026). Execution Governance 4.0: From Authorization-Bound Execution to Governed Effect Fabrics (Version 0.3.6.2). Zenodo.https://doi.org/10.5281/zenodo.22157731 Research programme: https://executiongovernance.org Status: Independent research and pre-standardization candidate. This publication does not constitute a standard, certification scheme, legal authorization determination, production-safety claim, or proof of governance completeness.
Ho Wa KU· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks offer a decentralized solution, enabling learning across multiple clients without direct data sharing. The proposed method involves local training of Bayesian Networks on individual client graph subsets, followed by parameter aggregation by a central server to refine a global Bayesian Network model. This architecture addresses the limitations of traditional GNN training while prioritizing data privacy and mitigating computational demands. The key innovation lies in the synergistic combination of federated learning principles with the probabilistic inference capabilities of Bayesian Networks, resulting in a robust and scalable framework for distributed graph learning. This approach demonstrates the potential for efficient learning from decentralized graph data sources.
Jincheng Zhang· 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.
