Safe: Synergic Data Filtering for Federated Learning in Cloud-Edge Computing

With the increasing data scale in the Industrial Internet of Things, edge computing coordinated with machine learning is regarded as an effective way to raise the novel latency-sensitive services. To ensure the data privacy for frequent service access, federated learning (FL), as a privacy-preserving distributed framework, is integrated into edge computing, enabling user data invisible to the training process. However, sophisticated network attacks threaten deep learning (DL) models by data poison and malicious reasoning, making the DL-based system untrustworthy. To this end, a synergic data filtering method, named Safe, is proposed to deal with the poisoning attacks. Specifically, considering that the distributed support vector machine is at risk of being attacked due to its distribution and openness to communication, edge-cloud empowered FL framework is designed. Then, the alternating direction method of multipliers is deployed to detect attacked devices whose training processes will be interrupted. Moreover, due to the untrustworthiness of label data, the poisoned data in the attacked devices are figured out and filtered by clustering the trusted data with K-means clustering algorithm. Eventually, extensive experiment results proved that the Safe outperforms correlation methods in detection accuracy and trustworthiness.