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

ArXi:2604.17371v1 Announce Type: cross 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.