LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Client Scheduling in Wireless Federated Learning Based on Channel and Learning Qualities

Photo from wikipedia

Federated learning (FL) emerges as a distributed training method in the Internet of Things (IoT), allowing participating clients to use their local data to train local models and upload parameters… Click to show full abstract

Federated learning (FL) emerges as a distributed training method in the Internet of Things (IoT), allowing participating clients to use their local data to train local models and upload parameters for global model aggregation after every few local iterations, protecting data privacy and reducing communication overhead. Given the scarcity of wireless communication resources, in this letter, we propose a client scheduling strategy for a wireless FL network based on a joint quality of channel and learning. Finally, we compare the proposed scheduling method’s performance with that of traditional methods considering the channel quality only. Experimental results show that our method can significantly improve training performance in terms of model accuracy and speed of convergence.

Keywords: channel learning; wireless; federated learning; scheduling wireless; client scheduling

Journal Title: IEEE Wireless Communications Letters
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.