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IronForge: An Open, Secure, Fair, Decentralized Federated Learning. (arXiv:2301.04006v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) provides an effective machine learning (ML)
architecture to protect data privacy in a distributed manner. However, the
inevitable network asynchrony, the over-dependence on a central coordinator,
and the lack of an open and fair incentive mechanism collectively hinder its
further development. We propose \textsc{IronForge}, a new generation of FL
framework, that features a Directed Acyclic Graph (DAG)-based data structure
and eliminates the need for central coordinators to achieve fully decentralized
operations. \textsc{IronForge} runs in a public and …
architecture data data privacy decentralized development distributed fair features federated learning framework machine machine learning network privacy protect