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The OPALS project focuses on optimizing split learning methods in artificial intelligence to enhance resource efficiency. By developing algorithms with performance guarantees, it aims to improve distributed machine learning while minimizing energy consumption.
With the rapid evolution of Artificial Intelligence, distributed machine learning methods such as Federated Learning (FL) are becoming ubiquitous in present-day technology.
In FL, devices train neural network models while data stays local. A central entity then aggregates the model updates into a global model.
Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to participate in this learning framework.
In a nutshell, SL splits the model into parts, and…
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