FED6G: Chameleon Learning for Network Slice Management in Beyond 5G Systems

A cornerstone of wireless connectivity involves trust and privacy in the data shared between users and network elements as wireless connectivity becomes an integrated, fundamental element of society. With a large influx of data in Beyond 5G (B5G) systems from end-users and network elements, it is imperative to understand how data is collected and used for real-time data processing operations. The current wireless network learning involves centralizing the training data, which is inefficient as it continuously requires end devices to send their collected data to a central server. Federated Learning (FL) effectively allows end devices to train ground-truth data on-device, and only model update parameters are sent back to the federated server. This work proposes a Chameleon FL model, FED6G, for network slicing in 5G and Beyond systems to solve complex resource optimization problems without collecting sensitive, confidential information from end devices. The evaluation results reflect more than 39% improvement in Mean Squared Error (MSE), 46% better model accuracy, and more than 23% reduced energy cost for training the proposed FED6G against the traditional deep learning neural network model.