The FEDn framework enables seamless development and deployment of federated learning (FL) applications, from local proofs-of-concept to distributed real-world settings.
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.
.jpg)
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.
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.
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.
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.
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.
