Leveraging Kernel Symmetry for Joint Compression and Error Mitigation in Edge Model Transfer

TL;DR

This paper introduces a symmetry-based compression method for neural networks that reduces bandwidth and enhances robustness during transmission, outperforming pruning methods especially under noisy conditions.

Contribution

It proposes a degrees-of-freedom codec leveraging kernel symmetry constraints for efficient, robust neural network transmission over constrained links.

Findings

DoF-based transmission reduces bandwidth significantly.

Symmetry-enforced projection improves robustness against noise.

Central-skew symmetry offers the best accuracy-compression balance.

Abstract

This paper investigates communication-efficient neural network transmission by exploiting structured symmetry constraints in convolutional kernels. Instead of transmitting all model parameters, we propose a degrees-of-freedom (DoF) based codec that sends only the unique coefficients implied by a chosen symmetry group, enabling deterministic reconstruction of the full weight tensor at the receiver. The proposed framework is evaluated under quantization and noisy channel conditions across multiple symmetry patterns, signal-to-noise ratios, and bit-widths. To improve robustness against transmission impairments, a projection step is further applied at the receiver to enforce consistency with the symmetry-invariant subspace, effectively denoising corrupted parameters. Experimental results on MNIST and CIFAR-10 using a DeepCNN architecture demonstrate that DoF-based transmission achieves substantial bandwidth reduction while preserving significantly higher accuracy than pruning-based baselines, which often suffer catastrophic degradation. Among the tested symmetries, \textit{central-skew symmetry} consistently provides the best accuracy-compression tradeoff, confirming that structured redundancy can be leveraged for reliable and efficient neural model delivery over constrained links.