Leveraging Kernel Symmetry for Joint Compression and Error Mitigation in Edge Model Transfer
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 ...
| Authors | Anis Hamadouche, Mathini Sellathurai |
|---|---|
| Primary category | eess.SP |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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