How can we improve communication efficiency in federated learning?

How can we improve communication efficiency in federated learning?

Improving communication efficiency in federated learning can be achieved through techniques like model compression, quantization, and reducing the frequency of updates.

What is model compression and how does it help?

Model compression reduces the size of the model by techniques like pruning or distillation, which decreases the amount of data sent during communication.

Can you explain quantization?

Quantization involves reducing the precision of the model weights, which lowers the data size and speeds up communication without significantly affecting performance.

What are some challenges in federated learning?

Challenges include data heterogeneity, communication costs, and ensuring privacy while maintaining model accuracy.