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Seed funding for data security with federated learning

As artificial intelligence (AI) rapidly evolves, the need for improved data security and personal privacy is more urgent than ever. Scaleout Systems, an innovative AI company, addresses this critical challenge and has successfully completed a significant seed funding round. This financing is intended to accelerate the company's development of its unique technology based on federated machine learning, enabling AI to reach its full potential without compromising user data privacy and integrity.

Blog posts

Enhancing IoT security with federated learning

We're integrating federated learning to create an innovative intrusion detection system that enhances privacy and threat detection. This approach promises a secure, privacy-focused IoT, leveraging decentralized data without compromise. More details in the post and follow for updates.

Understanding the Scaleout Software Suite

Diving into the world of federated learning, this text introduces and explores Scaleout's innovative software suite, specifically its key components, FEDn and Studio, and how they collaboratively deliver a powerful solution for federated machine learning initiatives.

Transforming System Developers into Smart Service Providers

Federated machine learning provides a solution to stringent data privacy regulations by allowing model training without centralizing data, turning developers into smart service providers.

Scaleout Systems Takes Data Security and Privacy to the Next Level with Federated Machine Learning

Scaleout Systems, tackling data security and privacy in AI, secured seed funding.

Shaping the Future of Edge AI

An overview of the Vinnova-sponsored project: Validating a System Development Kit for Edge Federated Learning.

Product Update November

The Scaleout Studio platform update includes a redesigned user interface for easier management of Federated Learning Networks, a streamlined process for adding clients, improved training session management, and better monitoring of session progress and model outcomes. The release also introduces a new REST-API, APIClient, and extensively updated documentation.

Product Update January

The FEDn version 0.7.0 introduces significant enhancements including the integration of Python's logging framework for client-side logging, compatibility improvements for Windows users, and substantial refactoring of the gRPC server for better scalability and efficiency. This update brings a more robust and user-friendly experience to our users, particularly in managing client-server interactions and data handling.

Output Privacy and Federated Machine Learning

As the field of machine learning evolves, the need for enhanced data privacy and security has given rise to federated machine learning. This approach decentralizes the data, addressing key privacy concerns and providing innovative solutions to traditional machine learning challenges. Explore how federated machine learning works, its benefits, potential risks, and the measures taken to fortify its security and privacy in the following discussion.

Our approach to scalable federated learning

Real-world federated learning software must be robust, resilient, and scalable, as well as being capable of handling large numbers of edge-clients and model sizes. Scaleout is designed to meet these demands.

FEDn Framework Extensions

Federated Learning merges client model updates into a global server model. It involves data transfer, aggregation like FedAvg, and focuses on secure, scalable implementation. FEDn enhances this with a flexible plugin architecture.

Efficiency vs. Privacy? You no longer have to choose.

In enjoying the convenience of AI tools, we've unknowingly traded data privacy, leaving trails of personal information ready for analysis. However, with the arrival of federated learning and data minimization principles, a new era calls where efficiency doesn't come at the cost of privacy, ensuring a balanced relationship between technological advancement and personal data security.

Improving the clinical workflow with federated learning

Right now, when doctors use software to help them with image-guided therapy, they have to do a lot of work themselves. Much of their focus goes towards the equipment rather than spending as much of their time as possible with the patient. This can be improved.

The Path to Compliant Machine Learning

In this article, we explore a recommended path towards responsible, compliant machine learning on regulated data, which involves applying data minimization principles and relevant privacy-enhancing technologies (PETs) to your ML systems.

Announcing Scaleout Studio

We're excited to announce Scaleout Studio - a key addition to our software suite. With Studio we offer a secure and privacy-enhancing ML platform that is simple to install, whether on-site or on a private cloud.

The Copy Problem in Machine Learning

Private AI as a concept aims to ensure that sensitive, private and regulated data remains secure even as it is used to train and deploy machine learning models.

Product Update January

This update brings a more robust and user-friendly experience to our users, particularly in managing client-server interactions and data handling.

AI and big data in cancer treatment

Discover how the ASSIST project is leveraging AI and Federated Learning to transform cancer treatment, focusing on efficient, privacy-compliant data use and advanced radiotherapy planning.

Seed funding for data security with federated learning

This funding marks a significant step for us in leading next-generation AI solutions.