Split federated learning system and method optimized for various hardware environments and local client for performing the same

A method for split federated learning performed on each local client of a system including a plurality of local clients each including a local model and a server including a global model, the local model including a personalized neural network and a federated neural network, and the federated neural network being provided with the same size and structure of the neural network in each local client, the method comprising:

• setting the number of parameters configuring the personalized neural network according to performance of the local client;
• performing a split learning process for parameter update of the personalized neural network;
• and
• performing a federated learning process for parameter update of the federated neural network.

A local client of a split federated learning system that includes a plurality of local clients each including a local model and a server including a global model,

• wherein the local model includes a personalized neural network and a federated neural network,
• the federated neural network is provided with the same size and structure of the neural network in each local client,
• the local client includes a memory in which one or more programs stored, and
• the one or more programs include:
• an instruction for setting the number of parameters configuring the personalized neural network according to performance of the local client;
• an instruction for performing a split learning process for parameter update of the personalized neural network;
• and
• an instruction for performing a federated learning process for parameter update of the federated neural network.