IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL’s further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

I. Introduction

Federated learning (FL), officially introduced by Google vanilla in 2017 [1], has become the preference to aggregate data from distributed ends without breaching data privacy [1], [2]. By aggregating huge data with comprehensive extracted features in FL, critical issues, such as model overfitting, can be significantly addressed [3]. However, the following hold: 1) the inevitable network asynchrony; 2) the overdependence on a central coordinator; and 3) the lack of an open and fair incentive mechanism hinder the further development of FL in large and open scenarios [4].