Resilient Federated Learning on Embedded Devices with Constrained Network Connectivity

What is it about?

At its core, the paper tackles the communication bottleneck in federated learning. Instead of forcing every device to transmit updates on a fixed schedule, the authors propose a more innovative, context-aware strategy: clients check how closely their local gradients align with the global model’s direction and only send highly compressed updates when the information is likely to be useful. By aligning communication with actual learning progress, the system reduces bandwidth requirements while maintaining model performance.

Featured Image

Photo by Solen Feyissa on Unsplash

Photo by Solen Feyissa on Unsplash