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Scaleout and Flower partner on federated learning solutions

Scaleout, the company behind the FEDn federated learning platform, and Flower, the open-source federated learning framework, have announced a strategic collaboration that enables developers to run Flower projects on FEDn, providing access to enterprise-grade security, scalability, and monitoring capabilities.

Blog posts

Enhancing data security with trusted execution environments

Trusted Execution Environments (TEEs) provide secure hardware-based protection for code and data, ensuring confidentiality and integrity. They are used to secure ML model serving, federated learning, and attestation processes. Performance benchmarks show Intel SGX excels with smaller applications, while AMD SEV handles larger workloads better.

Email Spam Detection with FEDn and Hugging Face

Our project uses the Hugging Face 'Transformers' library in FEDn to fine-tune a BERT-tiny model for spam detection on the Enron email dataset. Federated learning ensures data privacy by splitting the dataset between two clients. Training results in high accuracy (~99%) after a few rounds.

Federated Self-supervised Learning and Autonomous Driving

Federated self-supervised learning trains AI on autonomous vehicles using their sensor data without centralizing or labeling it. This method handles privacy, compliance, and data volume challenges by updating models directly on vehicles and training with unlabeled data.

Leveraging JWT Authentication for Secure Client and Admin API Access in FEDn Studio

About using JWT authentication in FEDn Studio to secure API access for clients and admins. It explains the JWT structure, authentication process, and its application in securing API endpoints, ensuring that only authenticated users can access and manage federated projects effectively.

Simplifying Federated Project Management with ArgoCD in FEDn Studio

Explaining how ArgoCD enhances FEDn Studio for managing federated projects on Kubernetes, using a custom helm chart for automation and consistency. This integration improves deployment efficiency, scalability, and oversight across multiple projects.

The impact of the backdoor attack

Learn more about backdoor attacks in federated learning, detailing experiments with the MNIST dataset that demonstrate the challenge of detecting such attacks when a minority of clients insert hidden triggers into the data. It also discusses potential mitigation strategies from recent research, emphasizing the ongoing security challenges in federated learning environments.

Federated Learning: Self-managed On-premise or SaaS?

Scaleout's FEDn offers SaaS and self-managed deployment options for federated learning, providing flexibility, security, and scalability to meet evolving user needs

Scaleout and Flower partner on federated learning solutions

We're announcing a strategic collaboration with Flower which enables developers to run Flower projects on FEDn, providing access to enterprise-grade security, scalability, and monitoring capabilities.

Input Privacy: Adversarial attacks and their impact on federated model training

Examination of the effects of label-flipping attacks on federated machine learning. Experiments show these attacks have a limited impact on the global model's accuracy compared to centralized training, due to the federated averaging process limiting malicious clients' influence.

Cross-device FL with FEDn Part 1

Using FEDn, this post demonstrates cross-device federated learning with intermittently connected clients. It sets up a local dev environment where clients randomly connect, train a model briefly, then disconnect - repeating the cycle. Results show FEDn can robustly train models under intermittent conditions, bridging research to production deployments.

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.