Fred Hutch researchers test privacy-first AI platform for cancer research Researchers at Fred Hutch Cancer Center are testing whether a collaborative AI research platform can accelerate the pace of cancer research leading to faster diagnoses and more precise, targeted thera...

Check out this terrific Hutch News article on the Cancer AI Alliance piloting their collaborative AI infrastructure. Eight projects are now testing federated learning models across four leading cancer centers.
www.fredhutch.org/en/news/cent...

#Cancer #AI #FederatedLearning

👋Hello from Ljubljana!

The #SwarmchestrateEU team is hosted at the University of Ljubljana, shaping the future of #CloudEdge orchestration.

We’re diving into #AI, #FederatedLearning, and real-world demos. Great energy solving complex problems in person! 🚀

🔗 swarmchestrate.eu

#HEU #Innovation

Premature Baby Nutrition & Neonatal AI: Isabel Hoffmann on Personalizing NICU Care Premature baby nutrition in most NICUs is standardized -- same fortification dose for every infant, regardless of gestational age, weight, or clinical profile. Isabel Hoffmann, CEO of TellSpec and Preemie Health, built a 30-second sensor that analyzes human milk and generates a personalized fortification prescription matched to each premature baby's clinical data. In this episode of Stories in Life Sciences, we cover: ✅Personalized neonatal nutrition: why standardized fortification over- or under-doses preterm infants ✅Human milk analysis: how near-infrared spectroscopy reads fat, protein, and fatty acids in 30 seconds ✅Federated learning in healthcare: training NICU AI models without sharing patient data across hospitals ✅AI model certification for medical devices: how the FDA freeze-and-resubmit process works ✅Synthetic data in medtech: how TellSpec trained early models using peer-reviewed literature ✅Neonatal intensive care technology: the gap between developed and developing country NICU standards ✅Human milk research gap: why we know less about human milk than dysfunctional erectile dysfunction and what that reveals about research priorities ✅Founder journey: from children's educational software and Apple bundling deals to neonatal AI Who this episode is for: Founders and researchers in medtech, neonatal care, digital health, AI in healthcare, and life sciences commercialization. ----------------------------------------------- Guest: Isabel Hoffmann, CEO - Tellspec & Preemie Health https://tellspec.com https://www.preemiesensor.com/ https://www.linkedin.com/in/isabelhoffmann https://www.linkedin.com/company/tellspec Host: Christopher Wilson, Founder - MedAxis AI www.medaxisai.org https://www.linkedin.com/in/christopher-wilson-medaxisai https://www.linkedin.com/company/medaxisai ----------------------------------------------- Subscribe for new episodes: https://www.youtube.com/@MedAxisAI #NeonatalCare #PrematureBaby #HealthcareAI #HumanMilk #MedTech #Biotech #LifeSciences #FederatedLearning

Our CEO @isabelhoffmann.bsky.social sat down on the Stories in Life Sciences podcast to discuss our federated learning platform—helping NICUs build early warning models for preterm disorders without sharing raw data.

🔗 tinyurl.com/yc78mkh8

#FederatedLearning #PretermInfants #NeoSky #MedTech

ZKFL-PQ combines ML-KEM, lattice-based ZKPs, and BFV homomorphic encryption to defend medical FL against Byzantine attacks and quantum threats, achieving 100% malicious update rejection with preserved model accuracy at ~20x overhead.

#PostQuantumCryptography #FederatedLearning #Research

75-paper review validates privacy-efficiency-trust trilemma: FL accuracy collapses 90%→21% under Byzantine attack; FHE imposes 5.7–28.4× overhead; hybrid FL+HE+Blockchain achieves 93.2% accuracy with 30% comms reduction.

#IoTSecurity #FederatedLearning #PrivacyPreserving

CQSA replaces fragile global GHZ entanglement with randomized small-cluster aggregation, achieving higher fidelity under NISQ noise while enabling inter-cluster Byzantine detection — a capability absent in existing Quantum Secure Aggregation protocols.

#QuantumComputing #FederatedLearning #Research

Image from article in Radiology: Artificial Intelligence

Giouroukou et al. review imaging data preparation for AI use: methods for protecting patient privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #deidentification #ML

#sustainlivwork #artificialintelligence #horizoneurope #horizoneu #centreofexcellence #bendraifinansuojaeuropossąjunga #cofundedbyeuropeanunion #dirbtinisintelektas #aitrends #mokslas #inovacijos… | S... EN | Sharing a few moments from AI TRENDS. This half-day event brought together the research community and partners to take stock of where AI is moving right now, and to sketch the priorities that wil...

This year's #AI Trends Event hosted by #SustAInLivWork covered topics including #data & #AIregulation, risk management, & trustworthiness. CVDLINK was represented by our partners from KTU, who delivered a presentation on #federatedlearning.
See highlights: www.linkedin.com/posts/sustai...

Federated Secure ML: A New Paradigm for Data Privacy In most enterprises today, the first instinct for building machine learning systems is still the same: gather data into a single, centralized lake, then train models on top. But that model of “bring all the data to the model” is colliding head-on with a new reality—one defined by stringent privacy laws, cross-border data residency restrictions, and deepening concerns about data breaches and model exploitation.

Federated ML lets organizations train powerful global models without moving sensitive data. Using Azure Confidential Computing + AWS Nitro Enclaves, we can secure training & inference end‑to‑end across clouds.
#FederatedLearning #CloudSecurity #AI #ConfidentialComputing #EdgeCompute #CloudDailywire

Image from article in Radiology: Artificial Intelligence

Federated learning, synthetic data generation, de-identification and more! A new review on imaging data preparation for AI https://doi.org/10.1148/ryai.250273 #privacy #FederatedLearning #deID

New IEEE TNSRE paper: Improving Generalization in #FederatedLearning for SSVEP Classification & Its Application in Soft Gripper.

🔗 ieeexplore.ieee.org/document/11271668

✨ ELIXIR features in both life science projects selected in the second oscarsproject.bsky.social open call 👉 https://loom.ly/T0DHrV8

🔬 MultiCellML-TDR - #FAIRness of computational models
🩺 FLEX4HEALTH - #federatedlearning across sensitive health datasets

Federated Learning, Part 2: Implementation with the Flower Framework 🌼

Implementing cross-silo federated learning step by step

Telegram AI Digest
#ai #federatedlearning #news

Федеративное обучение, часть 2: Реализация с помощью фреймворка Flower 🌼

Внедрение межсилосного федеративного обучения шаг за шагом

Telegram ИИ Дайджест
#ai #federatedlearning #news

Year in Review: Tech That Scales
From hardware to cloud, from concept to Core: DHDP delivered two platform streams in 2025—Flower and Core—enabling secure, privacy-by-design data sharing.

#AIML #federatedlearning #datasharing #CanSci

Image from article in Radiology: Artificial Intelligence

New Special Report! Methods for preparing imaging data for AI while protecting privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #deID #ML

Our Platform development is in expert and professional hands. Built by users for users
#HealthData #Innovation #DigitalHealth #CanadaTech
#AI #DataSharing #FederatedLearning #PrivacyByDesign
#PrecisionMedicine #HealthInnovation #CanadaResearch
#FutureReady #CanadaAI

Image from article in Radiology: Artificial Intelligence

Giouroukou et al. review imaging data preparation for AI use: methods for protecting patient privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #AI #deID

Federated Learning, Part 1: The Basics of Training Models Where the Data Lives

Understanding the foundations of federated learning

Telegram AI Digest
#ai #federatedlearning #news

Федеративное обучение. Часть 1: Основы обучения моделей там, где живут данные

Понимание основ федеративного обучения

Telegram ИИ Дайджест
#ai #federatedlearning #news

Image from article in Radiology: Artificial Intelligence

New Special Report! Methods for preparing imaging data for AI while protecting privacy https://doi.org/10.1148/ryai.250273 #privacy #FederatedLearning #ML

Image from article in Radiology: Artificial Intelligence

Giouroukou et al. review imaging data preparation for AI use: methods for protecting patient privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #MachineLearning #ML

I Evaluated Half a Million Credit Records with Federated Learning. Here’s What I Found

Why privacy breaks fairness at small scale—and how collaboration fixes both without sharing a single record

Telegram AI Digest
#ai #federatedlearning #news

Я оценил полмиллиона кредитных историй с помощью федеративного обучения. Вот что я обнаружил

Почему конфиденциальность нарушает справедливость в малом масштабе — и как сотрудничество исправляет и то, и другое, не раскрывая ни одной записи

Telegram ИИ Дайджест
#ai #federatedlearning #news

Decentralized AI in Healthcare!

Privacy-preserving, equitable, resilient. From federated learning to #blockchain governance—hybrid models are the #future

www.linkedin.com/posts/kumli_...

#healthcare #HealthTech #ai #federatedlearning

FedPOD: the deployable units of training for federated learning
Daewoon Kim, Jae Sung Lee et al.
Paper
Details
#FederatedLearning #FedPOD #DistributedTraining

Federated Learning Driving Secure Scalable Telecom Analytics Federated learning is driving secure and scalable telecom analytics, enabling privacy-first AI for 5G, edge networks, and data sovereignty.

Federated Learning Driving Secure and Scalable Telecom Analytics

read more : bi-journal.com/federated-le...

#FederatedLearning #TelecomAnalytics #BIJournal #BIJournalnews #BusinessInsightsarticles #BIJournalinterview

Decentralized AI vs. Big Tech: A New Power Shift The Unseen Battle: How Decentralized AI is Quietly Taking on the Tech Titans Let's be honest. When you think of artificial intelligence, a few names probably pop into your head.…

Decentralized AI vs. Big Tech: A New Power Shift #peertopeerAI #cryptoAIprojects #decentralizedartificialintelligence #BlockchainAI #Web3AI #AIethics #dataprivacyinAI #federatedlearning #techmonopolies #distributedmachinelearning

Understanding Federated AI Clouds: A New Era of Data Privacy In today’s data-driven economy, artificial intelligence (AI) thrives on vast amounts of information. Yet as industries such as healthcare, finance, and government become increasingly regulated, the traditional approach of centralizing data for model training faces insurmountable challenges. Privacy laws such as GDPR, HIPAA, and CCPA impose strict limitations on data movement, while organizations grapple with the ethical and security implications of sharing sensitive information.

Discover how Federated AI Clouds are transforming collaborative innovation without sharing raw data. Explore federated learning, secure enclaves, and distributed architectures shaping the future of AI in healthcare, finance, and beyond. #FederatedLearning #CloudDailywire #AIInnovation #PrivacyTech

Are you ready for the next #CoSyAdventcalender reading?
Say hello to FedProt, a privacy-preserving tool for multi-center differential protein analysis. #Proteomics #FederatedLearning

🔗 www.nature.com/articles/s43...