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Matched Averaging Federated Learning Gesture Recognition with WiFi Signals

WiFi signals based gesture recognition plays an important role in smart homes, such as human-computer interaction, identity authentication and so on. Most of existing approaches need a large number of channel state information dataset for model training to recognized human gestures. However, the robustness of the model to new users will be seriously affected, when the number of users participating in the training dataset is lacking. To address this problem, we propose a gesture recognition system based on matched averaging federated learning framework (WiMA). WiMA exploits parameter matching federated learning to training gesture prediction model, instead of traditional parameter aggregation. Experimental results show that the average accuracy of WiMA can be up to 90.4%, when 7 users participate in model training in 2 different rooms.