Data replication across all nodes to ensure high availability and fault tolerance, despite the potential increase in communication overhead.

Model compression techniques such as pruning and quantization to reduce the size of the model, without directly addressing the communication overhead.

Gradient aggregation, where each node computes gradients locally and only the aggregated gradients are communicated, significantly reducing the amount of data transferred.

Data sharding to distribute the dataset across nodes, which does not inherently reduce the communication overhead during model training.

None of the above options fully address the requirement for minimizing communication overhead while considering all given constraints.