Accelerating Distributed Deep Learning using Lossless Homomorphic
Compression
Accelerating Distributed Deep Learning using Lossless Homomorphic
Compression
As deep neural networks (DNNs) grow in complexity and size, the resultant increase in communication overhead during distributed training has become a significant bottleneck, challenging the scalability of distributed training systems. Existing solutions, while aiming to mitigate this bottleneck through worker-level compression and in-network aggregation, fall short due to their …