ChACo: Channel-Wise Adaptive Competitive Layer-Wise Learning

Local layer-wise learning offers modular optimization, layer-level transparency, and training without end-to-end error transport. However, its scalability remains limited by three coupled difficulties: local objectives can be weak or poorly aligned with the final task, shallow layers are often forced into premature fine-class discrimination, and fully local optimization is sensitive to activation and variance drift across depth. In this work, we present ChACo, a channel-wise adaptive competitive framework for fully local learning in convolutional networks. Each layer is trained with a local discriminative objective, while the internal block-to-class association is allowed to vary with depth. Early layers can map fewer competitive blocks to fine-label logits through a learned association policy, whereas later layers can recover direct fine-class competition as a special case. This design reduces the shallow-layer channel burden in many-class settings while preserving a direct local path to the final label space. The framework is supported by an analysis of local optimization dynamics, showing why even-power goodness functions and the ordering of rectification, convolution, and normalization are important for stable activation conditioning. We further incorporate lightweight stabilization components, including block-wise normalization and modular training schedules. Experiments on standard and many-class image-classification benchmarks show that ChACo improves over prior local-learning baselines, transfers across WAN and ResNet convolutional architectures, and narrows the gap to matched backpropagation (BP) references, while end-to-end BP remains stronger in the most depth-dependent settings. The source code of ChACo is available at https://github.com/andreaspapac/ChACo.