Scaleout

🚀 Exciting Opportunity Alert! 🚀 We're thrilled to announce that we still have 𝐭𝐰𝐨 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐬𝐥𝐨𝐭𝐬 𝐟𝐨𝐫 𝐨𝐧𝐥𝐢𝐧𝐞 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐭𝐡𝐞 𝟐𝐧𝐝 𝐒𝐮𝐦𝐦𝐞𝐫 𝐒𝐜𝐡𝐨𝐨𝐥 𝐨𝐧 𝐌𝐞𝐠𝐚𝐃𝐚𝐭𝐚: 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, hosted by the University of Tartu! 🏫 📅 Date: 28 July - 10 August 2024 📍 Location: Tartu (with online participation options) Join us for an incredible journey into the world of Federated Machine Learning, where you'll have the chance to: -Dive deep into cutting-edge research and advancements -Engage with leading experts in the field -Network with professionals and fellow enthusiasts from Flower Labs, Scaleout (FEDn), and Vantage6 framework. -Participate in interactive sessions and discussions Whether you're a seasoned professional or a passionate learner, this is a golden opportunity to expand your knowledge and stay ahead in the rapidly evolving field of Federated Learning. 🔗 Register Now to secure your spot! Contact me at: feras.awaysheh@ut.ee Details here: https://lnkd.in/dW5gmXJs Don't miss this chance to be part of a transformative learning experience. Two weeks of intensive FL course with only 600 euros registration. See you there! 🎓✨ #MegaData #FederatedLearning #MachineLearning #SummerSchool #UniversityOfTartu #OnlineLearning #DataScience #AI #BigData

Scaleout will be giving a federated machine learning (FedML) tutorial at the upcoming 33rd International Conference on Artificial Neural Networks, a conference of the European Neural Network Society. If you plan to attend the conference, don't miss the opportunity to learn about FedML, get an introduction to the FEDn framework, and, most importantly, participate in a hands-on session where we will jointly train a federated machine learning model. For more details, visit: https://lnkd.in/dCY7ZW-X #federatedlearning #ICANN24

Scaelout attends Almedalsveckan next week! Reach out to Ebba Kraemer for AI and Data Privacy discussions

This video provides a step-by-step guide on using the FEDn API client with FEDn Studio. Niklas Holmström demonstrates how to: ▶ Create and activate a virtual environment ▶ Generate an API token and set it as an environment variable ▶ Install the FEDn package via pip. After setting up, Niklas shows how to use the API client to list sessions and retrieve models from their project by running specific commands in a Python shell. https://lnkd.in/detFd63B #federatedlearning #demo

Federated Multi-task Learning (FMTL) combines federated and multi-task learning. Clients train local models and share only necessary parameters to maintain privacy. This approach handles data and system heterogeneity, ideal for scenarios like hospitals collaborating on different medical tasks while keeping data private. Learn more in this post by Jonas Frankemölle https://lnkd.in/dM82dGZw

A new version of FEDn Studio has been released, focusing on displaying information about training rounds for users. https://lnkd.in/dnQXA8v7 Niklas Holmström demonstrates the steps included: • Generate the compute package and upload files. • Connect a client: • Create a directory and set up a Python virtual environment. • Install necessary packages and start the client. • Run training rounds and monitor them through the sessions tab. Results are displayed in various graphs, showing the model’s performance and evolution with an overview of the generated models and their training times. #federatedlearning #demo

When will an AI model reveal your sensitive data? 👀 👉 https://lnkd.in/dY9dis4N AI models can leak training data–this is known. However, such leakage has mainly been observed in lab-like conditions that often favor the attacker. Today, there are few answers to the question of what the risks look like in real-life operations. In a collaboration between AstraZeneca, Sahlgrenska University Hospital, Region Halland, Scaleout, Syndata AB, RISE Research Institutes of Sweden, and AI Sweden, the project LeakPro targets critical questions about AI model data leakage: ▪ How can one assess the likelihood of an AI model leaking its training data?  ▪ How can the risk of this happening be mitigated?  ▪ How can the legal and technical definitions of risk be aligned to make informed decisions about AI model usage? “Sharing models offers significant benefits, but it also comes with inherent risks that can never be completely eliminated. That's why having a thorough risk assessment is crucial. Our goal is to estimate and minimize the risks of data leakage when sharing models, whether within a collaboration or through scientific publications,” says Ola Engkvist, Head of Molecular AI, AstraZeneca. Learn more about LeakPro: 👉 https://lnkd.in/dY9dis4N Johan Östman, Fazeleh Hoseini, Anders Thoresson, Magnus Kjellberg, Markus Lingman, Mats Nordlund, Helena Theander.

We're excited to share our latest research. We have mapped c.250 startups within the European data ecosystem and answered questions such as: - In which areas are the most startups being founded? 📊 - How have funding trends evolved over the last five years? 💰 - How many data infrastructure startups are AI-powered, and how are they using it? 🧠 (hint - it's A LOT and growing...) - What developments in the AI space could help or hurt data infrastructure businesses? ❌ Read the full report here: https://lnkd.in/d9CA6nQ5 #Data #AI #Startups #DataInfrastructure #EuropeanTech

Towards next-generation software for securing AI applications AI integration into digital infrastructure is accelerating, while cyber attacks are on the rise. As we become more dependent on AI, securing it is crucial. We currently have several projects in development to address this: ▶ LeakPro: Leakage profiling and risk oversight for machine learning models ▶ Interstice: Intelligent security solutions for connected vehicles, focusing on on-vehicle intrusion detection ▶ IoT IDS: Advanced Intrusion Detection System for IoT using federated learning ▶ Secure Enclaves (TEE): Ensuring trusted execution of machine learning on local clients using secure enclaves Learn more: https://lnkd.in/dhbkqfHx #AI #CyberSecurity #FederatedLearning #Innovation

Towards next-generation software for securing AI applications AI integration into digital infrastructure is accelerating, while cyber attacks are on the rise. As we become more dependent on AI, securing it is crucial. We currently have several projects in development to address this: ▶ LeakPro: Leakage profiling and risk oversight for machine learning models ▶ Interstice: Intelligent security solutions for connected vehicles, focusing on on-vehicle intrusion detection ▶ IoT IDS: Advanced Intrusion Detection System for IoT using federated learning ▶ Secure Enclaves (TEE): Ensuring trusted execution of machine learning on local clients using secure enclaves Learn more: https://lnkd.in/dhbkqfHx #AI #CyberSecurity #FederatedLearning #Innovation