The FEDn framework enables seamless development and deployment of federated learning (FL) applications, from local proofs-of-concept to distributed real-world settings.
In the AI landscape, securing machine learning models and their data is crucial.
Federated learning, a dual-use technology, is especially valuable for secure AI in defence tech applications. We have several exciting projects under development, enhancing both security and efficiency.
Integrating federated learning with additional security techniques, can significantly enhance the protection of AI applications, ensuring data privacy, regulatory compliance, and robust defence against threats.
From enhanced machine learning in cybersecurity and defence to privacy-centric AI in smartphones.
AI improves car performance and safety by learning from data to make smarter road decisions. Federated learning in edge computing processes data in vehicles for immediate responses, avoiding off-site data transmission.
We are happy to serve a diverse range of federated learning use cases. This section highlights a few of the organizations we've had the privilege of working with.
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Join us for an upcoming workshop on federated learning, a great first touch for those looking to learn more about the FEDn framework.
An online workshop focusing on federated machine learning with a step-by-step demonstration. During this workshop, we will guide you through a typical workflow for establishing a cross-silo ML federation within an enterprise. Additionally, we will provide an in-depth exploration of the unique capabilities of Scaleout Studio and how it seamlessly integrates with MLOps workflows.
Our framework offers an easy-to-use interface, visual aids, and collaboration tools for ML/FL projects, with features like distributed tracing and event logging for debugging and performance analysis. It ensures security through client identity management and authentication, and has scalable architecture with multiple servers and load-balancers. FEDn also allows flexible experimentation, session management, and deployment on any cloud or on-premises infrastructure.
FEDn is a versatile framework that can be extended, configured, and integrated into existing systems to tailored to your environment. For effective Federated Learning (FL) management, deployment of server-side components and charts is necessary. It enhances rather than replaces your current setup.
FEDn offers two main deployment options to cater to different organizational needs and project stages. The fully-managed SaaS (Software as a Service) model simplifies access to federated learning technology, making it ideal for early-stage projects, pilots, and proof-of-value phases. For organizations with strict cybersecurity requirements, FEDn can be deployed on private clouds or on-premise, providing full control over deployment, security, and privacy. This self-managed option is particularly suitable for advanced security needs, such as protecting IP-sensitive ML models.
Absolutely. You can develop your own IP without any conflict. Utilize our framework and Scaleout’s expertise to accelerate your project. There's no risk of lock-in, as our Software Development Kit (SDK) for integration is licensed under Apache2. We're confident you'll find value in our support services, warranty, indemnification, and comprehensive toolkit.
We offer a cloud-hosted FL platform for easy FL exploration, optimized for cost and ideal for R&D. Scaleout enables data scientists to investigate FL without initial IT/DevOps resources. We provide a smooth transition to self-hosted production with enterprise integrations, ensuring your PoC is scalable, secure, and representative of real-world scenarios.
FEDn supports a range of capabilities to meet diverse organizational demands. It offers both multi-tenant and single-tenant options, allowing organizations to choose the configuration that best suits their needs. Additionally, FEDn takes care of complex operations management, including server aggregation, data storage, user authentication, network configuration, and system monitoring. This reduces the operational burden on users, enabling them to focus on developing and refining their federated learning.
An article on using FEDn, a framework that enables federated learning by distributing training across multiple clients. It demonstrates how to package a PyTorch MNIST model for federated training using FEDn's "compute package" system.
Federated Learning (FL) with Differential Privacy (DP) is a privacy-focused machine learning approach. This post covers two main areas: the basics of Differential Privacy, including the application of Gaussian noise for enhanced privacy, and privacy measures in Federated Learning, addressing both record-level and client-level protections.
YOLO object detection models can be integrated with federated learning to enable privacy-preserving machine learning across distributed devices. This combination allows for real-time object detection while keeping sensitive data local, making it ideal for applications like autonomous vehicles, medical imaging, and manufacturing quality control.
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