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