Abstract
Distributed and collaborative machine learning over emerging IoT networks is complicated by resource constraints, device and data heterogeneity, and the need for personalized models that cater to the individual needs of each network device. This complexity becomes even more pronounced when new devices are added to a system that must rapidly adapt to personalized models. Along these lines, we propose a networked federated meta-learning (NF-ML) algorithm that utilizes meta-learning and underlying shared structures across the network to enable fast and personalized model adaptation of newly added network devices. The NF-ML algorithm learns two sets of model parameters for each device in a distributed manner, with devices communicating only with their immediate neighbors. One set of parameters is personalized for the device-specific task, whereas the other is a generic parameter set learned via peer-to-peer communication. The performance of the proposed NF-ML algorithm was validated using both synthetic and real-world data, and the results show that it adapts to new tasks in just a few epochs, using as little as 10% of the available data, significantly outperforming traditional federated learning methods.