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1,892 papers
ETD-FGL: Federated Graph Learning Against Backdoor Attacks Using Topology-Aware Defense This release provides the research implementation of ETD-FGL, a topology-aware defense framework for investigating structural backdoor attacks in federated graph learning. The software includes experimental pipelines for the Cora, P...
Cross-silo deepfake speech detection must accommodate organizations that observe different attack profiles and channel conditions while avoiding routine centralization of raw speech. We study federated learning (FL) for a compact detector combining linear-frequency cepstral coefficients (LFCCs) with frozen WavLM repres...
The El-Rakhawi Theory of Comprehensive Legal-Tech Harmony unifies AI automation, digital governance, socio-legal stability, economic resilience, and environmental compliance as topological dimensions of a single legal-technological ecosystem. It introduces the Comprehensive Legal-Tech Harmony Index (CLTHI): CLTHI = (A_...
Reliable anomaly detection is essential for the safe operation of unmanned aerial vehicles (UAVs), particularly in distributed settings where flight telemetry is generated across multiple vehicles or operational sessions and cannot always be centrally collected. This work investigates UAV anomaly detection using models...
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Dermatological disorders represent a massive global health burden, affecting approximately 2 billion people, yet conventional diagnosis often relies on morphology, leading to unpredictable, “trial-and-error” treatment. This necessitates a shift toward precision dermatology, leveraging the genomic and proteomic data tha...
The Cognitive Federated Graph Learning (CFGL) framework is introduced to jointly optimise three interdependent resources in cognitive radio networks: (i) frequency channel allocation for secondary-user transmissions, (ii) routing paths for multi-hop packet traversal, and (iii) transmit-power budgets as determined by pe...
The rapid growth of Internet of Things (IoT) networks has increased the need for intrusion detection mechanisms that provide accurate threat detection while preserving privacy, resisting malicious clients, and limiting communication overhead. Existing Federated Learning (FL)-based intrusion detection systems (IDS) rema...
The Federated Logistics Operations Dataset (FLOD) is a large-scale real-world dataset designed to support research on distributed logistics optimization, predictive modeling, and industrial Internet of Things (IIoT) analytics. The dataset consists of 253,020 operational records collected from geographically distributed...
The COVID-19 pandemic exposed persistent weaknesses in health-data interoperability, cybersecurity, provenance, cross-organizational governance, and continuity of digital care. This short communication presents an updated focused narrative synthesis and a five-layer governance framework for integrating blockchain and t...
The chapter discusses how blockchain-based verifiable credentialing, federated learning, and immersive metaverse environments converge as a single architectural solution to three structural failures in modern digital education: centralised credential vulnerability, privacy personalisation trade-off, and lack of experie...
The rapid advancement of smart textiles, flexible electronics, Edge Artificial Intelligence, and Tiny Machine Learningis transforming wearable healthcare systems from passive sensing platforms into intelligent, real-time health monitoring ecosystems. This chapter explores the integration of e-textiles, textile-based bi...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Learn more about our approach to safety and security for Gemini 4 Argon.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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