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• W4293588330 abstract "Federated learning is a collaborative and distributed machine learning model that addresses the privacy issues in centralized machine learning models. It emerges as a promising technique that addresses the data sharing concerns for data-private multi-institutional collaborations. However, most existing federated learning systems deal with centralized coordinators and are vulnerable to attacks and privacy breaches. We propose a blockchain-empowered coordinator-less decentralized, federated learning platform “Rahasak-ML” to solve issues in centralized coordinator-based federated learning systems by providing better transparency and trust. It uses an incremental learning approach to train the model by multiple peers in the blockchain network. Rahasak-ML is integrated into the Rahasak blockchain as its data analytics and machine learning platform. Each peer in the blockchain can establish supervised or unsupervised machine learning models with the existing data on its own off-chain storage. Once a peer generates a model, it can be incrementally/continuously trained and aggregated by other peers through the blockchain using the federated learning approach without requiring a centralized coordinator. The model parameters sharing, local model generation, incremental model training, and model sharing functions are implemented in the Rahasak-ML platform. We discussed the promise of Rahasak-ML machine learning in medicine." @default.
• W4293588330 created "2022-08-30" @default.
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• W4293588330 date "2022-01-01" @default.
• W4293588330 modified "2023-10-01" @default.
• W4293588330 title "A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery" @default.
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• W4293588330 doi "https://doi.org/10.1007/978-981-19-2976-2_6" @default.
• W4293588330 hasPublicationYear "2022" @default.
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