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Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances.

Federated learning can collaboratively train a global model without gathering clients’ private data. Many works focus on reducing communication cost by designing kinds of client selection method or averaging algorithm. But they all consider whether the client will participant or not, and the training time could not be reduced as data size of update for each client is not changed. We proposed COFEL, a novel federated learning system which can both reduce the communication time by layer-based parameter selection and enhance the privacy protection by applying local differential privacy mechanism on the selected parameters. We present COFEL-AVG algorithm for global aggregation and designed layer-based parameter selection method which can select the valuable parameters for global aggregation to optimize the communication and training process. And it can reduce the update data size as only selected part will be transferred. We compared with traditional federated learning system and CMFL which also applies a parameter selection method but model-based and performed experiments on MNIST, Fashion-MNIST and CIFAR-10 to verify the effectiveness of COFEL. The results denoted that it can improve at most 22.8% accuracy compared with CMFL on CIFAR-10 and reduce around 20% and 48% training time to reach an accuracy of 0.85 compared with traditional FL and CMFL on Fashion-MNIST dataset.