Over-the-Air Personalized Federated Learning
Hasin Us Sami, Başak Güler · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
Federated learning is a distributed framework for training a machine learning model over the data stored by wireless devices. A major challenge in doing so is the communication overhead from the devices to the server. Over-the-air federated learning is a recent framework to address this challenge, which utilizes the superposition property of the wireless multiple access channel to enable computations to be performed in the wireless medium. Current over-the-air aggregation frameworks, on the other hand, train a single model for all users, which can degrade performance in heterogeneous environments where the data distributions of the users can differ from one another. This work presents a personalized over-the-air federated learning framework towards addressing this challenge. Our experiments demonstrate significant performance improvement in terms of the test accuracy over conventional federated learning.