SplitFed: When Federated Learning Meets Split Learning

Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Additionally, as in SL, its communication efficiency over FL improves with the number of clients.

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• 1 Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data.
• 2 SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.
• 3 Moreover, the split model makes SL a better option for resource-constrained environments.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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