News & Updates

Fighting Back Against Attacks in Federated Learning

Federated Learning (FL) is reshaping how AI models are trained. Instead of gathering data in one central place, each device trains locally and only shares model updates. This approach protects

From Satellites to Fleets: Our Ongoing Research Initiatives

Overview highlighting how communication efficiency can be improved across multiple levels from GEO (Geostationary Earth Orbit), to LEO (Low Orbit Earth) and base stations. At Scaleout, research and development are

Federated Learning with 10,000 Asynchronous Clients Using Scaleout Edge

We are releasing a new toolkit aimed to support large-scale development of cross-device federated learning applications. In this post we demonstrate cost-effective simulations of 10 000 intermittently connected clients, each

Data Selection on the Edge for Adaptive Federated Machine Learning

In federated machine learning, one of the biggest challenges is ensuring that models continue to adapt and improve without overwhelming the system with unnecessary or redundant data. Sending raw video

Collaborative AI for Lung Cancer Detection: Federated Learning in Healthcare Without Sharing Patient Data

In modern healthcare, AI promises faster diagnoses, better treatment plans, and more efficient clinical workflows. This inherently complex process further gravitated due to restricted data access. Hospitals cannot simply pool

Fleet Intelligence with Mixture-of-Experts Federated Learning

As connected vehicles, drones, and industrial devices continue to generate vast amounts of data, industries face growing challenges in processing this information efficiently, while respecting privacy and minimizing communication costs.

Vertical Federated Learning with FEDn

We would like to thank Vinnova , Sweden’s innovation agency, for funding this project. TL;DR: Vertical Federated Learning allows clients with different but complementary features for the same samples to

Exploring Python vs. C++ Clients - A Performance Deep Dive with FEDn Framework

Federated learning (FL) is transforming machine learning by enabling decentralized training across multiple devices. In this post, we will explore some performance insights using the FEDn framework, comparing Python and

Scaleout secures new investment round to accelerate Cloud-Edge AI

Scaleout Systems, an innovator in federated learning solutions, has raised 35 MSEK in its latest funding round to accelerate its AI technology development and expand its presence in the cloud-edge

Distributed Continuous Machine Learning

Organizations today face a key challenge: effectively implementing machine learning in distributed environments while ensuring communication efficiency, data security and continuous learning. Traditional centralized methods are no longer sufficient for

Machine Learning in the Cloud-Edge Continuum

The computing landscape is changing. Organizations are producing large amounts of data at the edge, and the traditional centralized machine learning approach is reaching its limits. The future of AI

Scaleout Joins NATO's DIANA Programme to Advance Federated Intelligence in Conflict Zones

Scaleout has been selected to participate in NATO’s DIANA Challenge Programme with the FEDAIR (Federated Aerial Intelligence for Recon) project. This recognition highlights the growing importance of innovative solutions for

Federated Learning Made Easy with FEDn

A hands-on walkthrough of federating a PyTorch MNIST model with FEDn — building the compute package (data, model, train, validate) step by step.

Federated Learning for Object Detection Using YOLO

Discover how to leverage Ultralytics YOLO models in a federated learning environment for privacy-preserving object detection. Learn practical implementations for defect detection and animal classification. Introduction In recent years, the

Hyperparameter tuning with Optuna and FEDn Python API

2024-09-13 by Benjamin Åstrand In machine learning, hyperparameter tuning plays a crucial role in optimizing model performance. When it comes to federated learning, the need to tune hyperparameters not only

Enhancing Semiconductor Component Placement with Federated Learning

Juan Albahaca Precision is crucial in the field of semiconductor manufacturing. Mycronic, a leader in this domain, is exploring cutting-edge approaches to enhance their Pick and Place (PnP) machines, which

Federated Multi-task Learning

Federated Learning (FL) In federated learning, multiple clients collaboratively train a machine learning model without sharing their local data. Instead of sending raw data to a centralized server, clients train

Enhancing data security with trusted execution environments

TEE Project Summary Introduction The confidential computing consortium defines a Trusted Execution Environment (TEE) as a hardware-based context which provides a certain level of assurance for code and data confidentiality

Email Spam Detection with FEDn and Hugging Face

Our new example project demonstrates how one can make use of the popular Hugging Face ‘Transformers’ library in FEDn. In this example, a pre-trained BERT-tiny model from Hugging Face is

Federated Self-supervised Learning and Autonomous Driving

Autonomous vehicles generate a massive amount of data from sensors like cameras, LiDAR, and radar. This data can be highly valuable for AI development as it contains information about day-to-day

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

In the dynamic landscape of modern web development, security stands as a paramount concern. With the proliferation of APIs driving interactions between clients and servers, implementing robust authentication mechanisms is

Simplifying Federated Project Management with ArgoCD in FEDn Studio

In the realm of distributed systems and federated projects, managing deployments efficiently across multiple environments and projects on Kubernetes can be a daunting task. However, with the advent of tools

The impact of the backdoor attack

This is the second part of our input privacy series, focusing on the implications of established adversarial attacks on federated learning. It's worth noting that while extensive literature exists regarding

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

Scaleout offers federated machine learning solutions that maintain data ownership and privacy without central data pooling. Our federated learning framework, FEDn, supports machine learning across distributed datasets like data silos

Scaleout and Flower partner on federated learning solutions

Scaleout, the company behind Scaleout Edge, the enterprise-grade federated learning platform, and Flower, an open-source federated learning framework, today announced a strategic collaboration. This partnership is to enable developers to

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

Part 1 - The impact of label-flipping attack This blog post emphasizes the significance of examining the adversarial attack landscape for federated model training as a separate entity from the

Edge AI: A Comprehensive Guide to Real-Time AI at the Edge

Edge AI is an emerging field that combines artificial intelligence with edge computing, enabling AI processing directly on local edge devices. It enables real-time data processing and analysis without constant

Guaranteeing Data Privacy for Clients in Federated Machine Learning

How differential privacy strengthens federated learning — the (ε, δ)-DP definition, a worked Gaussian-mechanism example, record- vs client-level DP, and a DP-SGD example in FEDn with Opacus.

Federated Learning: Train AI Without Moving Data

Most AI systems are built on a simple but increasingly problematic assumption: that the data needed to train a model can be collected and moved to a central location. For

Scalable Federated Learning with FEDn: A Comprehensive Overview

Introduction Federated Machine Learning (FedML) has emerged as a promising approach to machine learning that addresses data privacy concerns by enabling model training across distributed data sources without sharing the