@inproceedings{7f820dc4665941518bd68bf3da4c5c7c,
title = "FLeX: Trading edge computing resources for federated learning via blockchain",
abstract = "Federated learning (FL) algorithms provide privileges in personal data protection and information islands elimination for distributed machine learning. As an increasing number of edge devices connected in networks, we still see a lot of computing resources and data remaining underutilized and there is no platform for users to trade FL tasks. In this demonstration, we propose a blockchain-based federated learning application trading platform called FLeX, on which users can buy and sell computing resources for training machine learning models with no sacrifice of data privacy. We design FLeX in a highly distributed and scalable manner. We separate the data plane and control plane in the platform. In FLeX, trading mechanisms and FL algorithms are deployed in smart contracts of the blockchain. Control messages and trading information are well protected in the blockchain. With FLeX, we realize a distributed trading platform for executing FL tasks.",
keywords = "Blockchain, Federated learning",
author = "Yang Deng and Tao Han and Ning Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 ; Conference date: 09-05-2021 Through 12-05-2021",
year = "2021",
month = may,
day = "10",
doi = "10.1109/INFOCOMWKSHPS51825.2021.9484628",
language = "English (US)",
series = "IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021",
address = "United States",
}