Over-the-Air Personalized Federated Learning
- Resource Type
- Conference
- Authors
- Sami, Hasin Us; Guler, Basak
- Source
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on. :8777-8781 May, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Wireless communication
Training
Computational modeling
Distributed databases
Signal processing
Collaborative work
Data models
Over-the-air machine learning
distributed training
personalized federated learning
- Language
- ISSN
- 2379-190X
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.