EADRAN: An Edge Marketplace for Federated Learning

The proliferation of edge data availability alongside advanced federated and distributed machine learning training techniques calls for new developments of machine learning (ML) with distributed, private edge data providers. Most existing works, however, focus on federated ML communication and aggregation techniques and under-research the quality of training based on the impact of the quality of data and contributions of distributed data sources from such providers to the building of the ML models. In this paper, we introduce an Edge marketplAce for DistRibuted AI/ML traiNing (EADRAN), a comprehensive platform for federated learning with independent edge data providers. The key distinguishable feature of EADRAN is to enable the explainable quality of training (eQoT) approach based on the quality of data and the contributions of provided data to the target-trained ML models. EADRAN offers end-to-end services for both data providers and ML market consumers, enhancing explainability and incentivizing the active participation of edge data providers. EADRAN shows high adaptability via the integration with notable FL frameworks like Flower to allow consumers to choose their preferred FL methods. EADRAN is developed based on a detailed conceptual architecture for federated machine learning marketplaces, guided by key principles and requirements based on the quality of training. We present the detailed design and implementation of EADRAN and conduct extensive experiments to demonstrate the benefits of eQoT-aware training in the ML marketplaces with heterogeneous data scenarios.