Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reco...
Dallas Courchene· Zenodo (CERN European Organi...· 0 citations
Clinical modeling experience is established as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.
A client-specific adaptation channel based on private prompt tokens, which tracks local adaptation dynamics separately from the shared backbone and provides a lightweight signal for detecting whether client adaptation remains active, and a shallow sufficiency estimator that combines cross-client semantic alignment, tem...
Wen-Hao Yuan, Chen-Chen Lin, Wentao Hu et al.· ACM International Conference...· 0 citations
Reproducibility capsule for FusionNet Lite, a lightweight sequence-fusion deep-learning pipeline for predictive maintenance in industrial IoT systems. The campaign evaluates FusionNet Lite alongside CNN, BiLSTM, MLP, and CNN-LSTM baselines using centralized and FedAvg federated training across five seeds. The capsule i...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Acute lateral ankle sprain is among the most frequent injuries in sports medicine, and its high recurrence rate and propensity toward chronic ankle instability (CAI) constitute a persistent clinical challenge. Conventional risk assessment—relying on subjective questionnaires, physical examination, and clinical experien...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devic...
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 schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. Th...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Residential load forecasting underpins demand response and local energy trading, yet privacy regulations such as the EU GDPR prevent service providers from collecting fine-grained household consumption data. Federated learning (FL) enables collaborative training without raw data exchange, and client clustering widely m...
Ran Zheng, Sara Barja-Martínez, Mònica Aragüés‐Peñalba et al.· Energy Reports· 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 schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. Th...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border f...
Vakhabov Bobur· Zenodo (CERN European Organi...· 0 citations
Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term di...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
