Fully-Decentralized Consensus-Based Federated Learning for Cell Outage Detection in Cellular Networks

Cell Outage Detection (COD) mechanisms in 5G and beyond cellular networks play an increasingly important role in ensuring uninterrupted services to end users by promptly identifying possible outages at the radio and cell levels. Traditionally, COD algorithms have used aggregated data at the core network level to detect anomalies, but there have been scalability and data confidentiality issues. This work proposes a novel fully-decentralized consensus-based Federated Learning approach. This approach utilizes Random Trees and federated feature removals to identify anomalies at the cell level. It is based only on data available locally at the Base Station (BS), but relies on knowledge acquired by all BSs participating in the federation. The approach is fully decentralized in the sense that it does not involve a central entity responsible for aggregating the knowledge of the learning agents. A set of simulations based on a dataset with real cell data has been employed to demonstrate the effectiveness of the proposed approach in comparison to other baseline approaches, even in the presence of malicious agents attempting to disrupt the learning process.