Scalable federated machine learning with FEDn

TL;DR

FEDn is a scalable, flexible framework for federated machine learning that supports both cross-device and cross-silo training, addressing key challenges like scalability, robustness, and security in distributed settings.

Contribution

The paper introduces FEDn, a new federated learning framework designed to enhance scalability, robustness, and security for geographically distributed machine learning applications.

Findings

Supports both cross-device and cross-silo training

Addresses scalability, robustness, and security challenges

Enables realistic federated learning research

Abstract

Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental aspects such as scalability, robustness, security, and performance in a geographically distributed setting. To bridge this gap we have designed and developed the FEDn framework. A main feature of FEDn is to support both cross-device and cross-silo training settings. This makes FEDn a powerful tool for researching a wide range of machine learning applications in a realistic setting.