Anomaly Detection through Unsupervised Federated Learning. (arXiv:2209.04184v1 [cs.LG])

Federated learning (FL) is proving to be one of the most promising paradigms
for leveraging distributed resources, enabling a set of clients to
collaboratively train a machine learning model while keeping the data
decentralized. The explosive growth of interest in the topic has led to rapid
advancements in several core aspects like communication efficiency, handling
non-IID data, privacy, and security capabilities. However, the majority of FL
works only deal with supervised tasks, assuming that clients' training sets are
labeled. To …

anomaly anomaly detection arxiv detection federated learning unsupervised