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• W3184992308 abstract "Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smart contracts on a blockchain to define incentivization rules for the contributors, which is based on a novel scalar quantity - federated contribution. Such a smart contract based reward-driven model has the potential to revolutionize the federated learning adoption in enterprises. Our contribution is two-fold: first is to show how smart contract based blockchain can be a very natural communication channel for federated learning. Second, leveraging this infrastructure, we can show how an intuitive measure of each agents' contribution can be built and integrated with the life cycle of the training and reward process." @default.
• W3184992308 created "2021-08-02" @default.
• W3184992308 creator A5008095998 @default.
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• W3184992308 date "2021-07-19" @default.
• W3184992308 modified "2023-09-27" @default.
• W3184992308 title "Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach." @default.
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• W3184992308 hasPublicationYear "2021" @default.
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