Category: Federated Learning
End-to-End Prototype using AffectLog’s Privacy-Preserving Mental Health Digital Twin Platform
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Introduction Integrating personal health data with clinical records can greatly improve the prediction and management of mental health conditions. In this prototype, we design a privacy-preserving mental health digital twin platform that unifies data from iPhone sensors, doctor’s notes, and electronic health records (EHR) to create a dynamic digital representation…
AL360°: Redefining Trust and Access in the Private Data Economy
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In a data-centric world marked by surging digital footprints and ever-tightening regulatory frameworks, enterprises, researchers, and policymakers face a critical dilemma: how to responsibly access and utilise sensitive, private data without breaching trust, security, or legal boundaries. This challenge is particularly pronounced in domains like healthcare, financial services, education, and…
PersonaCore: A Federated Neuromorphic Edge AI Platform for Privacy-Preserving Affective Digital Twins
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Abstract Affective Digital Twins (ADT) are virtual replicas of human emotional and cognitive states, enabling simulation and personalized AI interactions in human-centric applications. This paper presents AffectLog PersonaCore, a technical framework for implementing ADTs via a federated, privacy-preserving, neuromorphic computing platform. We detail how PersonaCore innovates on-device federated neuromorphic processing…
Affective Digital Twin: Advancements and Future Directions
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Affective Digital Twin Concept Affective Digital Twin (ADT) refers to a digital replica of an individual that encapsulates not only their physical state but also their cognitive and emotional characteristics. It builds upon affective computing (which enables machines to recognize and simulate human emotions) and digital twin technology (virtual models…
Federated Learning within the leading American Hospital Network: How AffectLog Sets a New Standard in Secure Data Aggregation for the Mental Health Monitoring of the Frontline Health workers
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In today’s data-driven landscape, healthcare data is a treasure trove of insights waiting to be unlocked—especially for large hospital networks. From predictive analytics in oncology to AI-based radiology, the potential for machine learning (ML) to enhance patient care is immense. Yet concerns around data privacy, complex regulatory requirements, and the…
