Federated Learning for Frequency Modulated Continuous Wave Radar Gesture Recognition for Heterogeneous Clients
Sprache des Titels:
Englisch
Original Buchtitel:
Proceeding of the 10th International Electronic Conference on Sensors and Applications
Original Kurzfassung:
Federated learning (FL) is a field in distributed optimization. Therein, the collection of data and training of neural networks (NN) are decentralized, meaning that these tasks are carried out across multiple clients with limited communication and computation capabilities. In FL, the client NNs are first trained with locally available data. Next, they are aggregated to update a global NN. FL suffers from non-independent and identically distributed (iid) data and asynchronous communication between the server and the clients, which degrades the NN?s overall performance. In this work, we investigate FL for a small live gesture sensing NN, using a low-power 60 GHz frequency modulated continuous wave radar from Infineon Technologies. The challenges of data sparsity, i.e., only a fraction of a gesture recording corresponds to an executed gesture combined with non-iid data, pose issues during neural network training. It is shown that FL reaches an accuracy higher than 96.2% for an iid setting. However, an increasing level of non-iid data degrades the accuracy to 64.8%. To tackle the accuracy degradation, we propose to dynamically adapt the class weights during the training procedure based on each client?s varying ratio of data sparsity. Moreover, regularization terms are included in the loss function to prevent client drift and overconfidence in the client?s NN prediction. Finally, it is shown that the proposed modifications increase the NN?s performance, such that an accuracy of 97% is obtained despite a high degree of non-iid data.
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