Abstract: Split learning (SL), an emerging distributed deep learning paradigm, enables collaborative model training without exposing clients’ raw data by partitioning neural networks between clients ...
The DIY Tools MCP server enables you to dynamically add custom tools without needing to write a full MCP server. Simply provide the function code, parameters schema, and the server handles the rest - ...
Abstract: In Split learning (SL), a promising edge learning, edge devices and a server split a neural network and jointly train by exchanging smashed data and corresponding gradients. To alleviate the ...
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