Split learning is a decentralized machine learning technique where a neural network model is divided into two parts, with one part trained on a client device and the other on a server or a decentralized network. Only intermediate data representations, not raw sensitive data, are shared between the client and server. This method enhances data privacy and reduces computational load on individual devices. It facilitates collaborative model training.
Context
Split learning is a key topic in discussions about privacy-preserving AI and decentralized machine learning, particularly for applications involving sensitive user data. Debates often focus on optimizing communication overhead and ensuring model accuracy across distributed components. Future research aims to further improve the efficiency and security of split learning for real-world decentralized AI applications.
Introducing a blockchain-enabled, sharded architecture with committee consensus to secure and scale distributed machine learning against centralized vulnerabilities.
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