Federated Machine Learning
Lead story · Science
Federated Machine Learning
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated tran…
ACM Transactions on Intelligent Systems and Technology
Gain
Secure federated learning allows organizations to build data networks and share knowledge without compromising user privacy.
Problem
AI progress is blocked because industry data remains in isolated islands and privacy and security constraints are strengthening.
Reading of the Month
Both headlines are true
AI out-diagnoses physicians and saves them almost no time; 170 million new roles are projected while young developers lose ground. July's reading holds both measurements in view.
Mosisa Saba · July 2026Read the essay →
Top stories
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Overcoming catastrophic forgetting in neural networks
Proceedings of the National Academy of Sciences
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Przegląd Bezpieczeństwa Wewnętrznego
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How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape
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AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases
Graefe's Archive for Clinical and Experimental Ophthalmology
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Human-AI co-creation or conflict? Mapping art students’ diverse perspectives on creative identity with genAI: A Q methodology study
Education and Information Technologies
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Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems
Health
Evidence-backed gain
Random Forest
For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of "big data" and machine learning, survival analysis has become methodologically broader. This paper aims to explore one t…
Systemic vascular and neurodegenerative disorders are important causes of disability and death worldwide, mainly because of the late stage of diagnosis and the high cost of current screening tools. Artificial intelligence (AI) and multimodal retinal imaging offer a non-invasive and viable approach for early risk stratification and longitudinal monitoring. This review highlights how changes in the retinal vasculature and nerve layers are markers of underlying pathophysiologies related to cardiovascular, metabolic…
#### Evolving surgical teams in the age of artificial intelligence and robotics
Surgery is a critical function of the healthcare system, key to addressing a substantial portion of the global disease burden. The integration of advanced artificial intelligence (AI) and robotics ecosystems into the operating room (OR) promises to radically transform surgery, with profound implications. This article analyzes the current state of surgical AI and robotic systems; presents a vision for their future, highlighting technological and research challenges and their associated impact on surgical teams; a…
#### Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems
Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and locally deployable machine learning and artificial intelligence (ML/AI) models remains uncertain. We characterized major US national EHR networks and examined barriers to ML/AI development and deployment across the LHS cycle. Methods: We conducted an environmental scan combining PubMed searches…
Background: Artificial intelligence (AI) is widely used in mental health care for screening, monitoring, and intervention. Notably, most studies of AI in mental health have been performed in Western contexts, with limited evidence from the Arab Gulf region, where cultural factors such as stigma, privacy, and help-seeking norms may influence acceptance. Objective: Investigating university students’ perceptions of AI in mental health support, including awareness, trust, readiness, and preferences in a Gulf context…
(1) Background/Objectives: Over one billion individuals globally live with mental health conditions, yet the treatment gap exceeds 75% in low- and middle-income countries. Large language model (LLM)-based conversational agents have emerged as a potentially scalable solution, though the evidence base remains nascent and largely pre-clinical. This review synthesises barriers and facilitators to their implementation in mental healthcare using the Consolidated Framework for Implementation Research (CFIR). (2) Method…
Education
Evidence-backed gain
The impact of robot-assisted language learning (RALL) on EFL students’ classroom engagement and willingness to attend classes (WTAC): A technology acceptance model (TAM) perspective
Recently, a surge of scholarly attention has been paid to the impacts of Artificial Intelligence (AI) tools on second/foreign language (L2) education. Nonetheless, there is a lack of evidence on the role of AI-assisted robots or robot-assisted language learning (RALL) in English as a foreign language (EFL) students’ psycho-emotional factors and academic behaviors. To address this gap, drawing on the technology acceptance model (TAM), the present study probed into the impact of robot-assisted L2 education on Chin…
Abstract Generative AI now mediates core parts of learning, yet we lack criteria to tell its legitimate pedagogical uses from manipulative and deceptive ones. We also know too little about how AI reshapes the growth of critical thinking and creativity, or about whether it accelerates drift from educational goods to evaluative metrics. Using a postdigital, pragmatist lens that treats classrooms as sociomaterial assemblages of people, platforms, and institutions, we propose three orienting principles: moral legiti…
Abstract Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative iden…
#### AI-enhanced pedagogical practices and mathematical language proficiency in STEM education
This study investigates the effectiveness of AI-enhanced pedagogical practices on improving the mathematical language proficiency of senior high school students in Ghana. The current research adopted a quantitative cross-sectional design, where the structural equation modeling approach was used on a sample size of 360 participants, to investigate digital literacy and learning engagement as partial mediators. As expected, results from this study show that AEPP, DL, and LE are statistically significant positive pr…
The deployment of AI tools in social work practice settings is advancing at a pace that social work education has yet to match. Drawing on baseline findings from an independent evaluation of Magic Note, an AI-assisted recording tool deployed across a Scottish Local Authority social work department, this paper uses the empirical experiences of a workforce encountering AI in practice as a window into the underdeveloped implications for qualifying education. The evaluation, which involved 152 practitioners surveyed…
Abstract Performance-based assessments such as oral presentations and viva voce exams are valued for their pedagogical benefits but can also be associated with heightened student anxiety, which may affect a range of learners and hinder student motivation and overall assessment experience. This study explores the potential of AIvaluate; an emotionally intelligent, LLM-augmented conversational agent, to provide a more supportive assessment environment for such learners. Using a counterbalanced quasi-experimental,…
Entertainment & the evidence leaderboard
Entertainment
Do deepfakes, digital replicas and human digital twins justify personality rights?
Abstract Unauthorised deepfakes are deeply problematic, from the spreading of misinformation to non‐consensual pornographic content. This paper asks whether deepfakes, digital replicas and human digital twins justify personality rights. To address this question, it examines the harms that deepfakes can cause through disinformation, demeaning content and displacing creative workers. It demonstrates that the current UK legal patchwork of passing off, intellectual property, defamation, and criminal laws do not adeq…
The Journal of World Intellectual Property
Top evidence-backed gains
1. In image-heavy specialties with standardized, digitized data, AI has reduced diagnostic time by approximately 90% or more while improving accuracy.Score 83 · 3 sources 2. In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics.Score 82 · 4 sources 3. In benchmark experiments reported by May 2026, the unified framework increased supply chain disruption-prediction accuracy to 94.1%, reduced demand-forecast error, and raised marketing campaign ROI from 14.2% to 45.3%.Score 80 · 1 sources
Top evidence-backed problems
1. 7.2% of evaluated MCP servers contained general vulnerabilities and 5.5% exhibited MCP-specific tool poisoning, part of eight distinct vulnerabilities largely distinct from traditional software flaws.Score 83 · 1 sources 2. AI-assisted retinal analysis faces implementation hurdles including lack of multicenter validation, need for prospective clinical trials, and unresolved data fusion and regulatory requirements.Score 79 · 1 sources 3. AI integration in surgery risks liability gaps from diluted authority chains and bias that exacerbates health inequalities, compounded by concentration of research in resource-rich nations.Score 79 · 1 sources