A Standard for the Training and Inference of Machine Learning Models via Avian Parameter Carriers

Current machine learning infrastructure relies heavily on high- bandwidth digital interconnects for parameter synchronization, gradient aggregation, and model weight distribution. This document proposes an alternative: Avian Parameter Carriers (APC), building on the physical transport layer established in RFC 1149 and the quality- of-service extensions of RFC 2549. The authors note that the bandwidth-delay product of a pigeon carrying a 512GB NVMe drive over 5 kilometers remains competitive with certain cloud providers during peak billing hours. This specification is considered Feature Complete. It addresses scale (Section 14), observability (Appendix A), security (Sections 11 and 11.4), regulatory compliance (Section 9.3), and pigeon hygiene (Sections 3.3, 10.4, and 11.4.5). Future revisions MAY address quantum parameter transport (Section 14.6) and UV-spectrum adversarial plumage design (Section 11.4.2). The pigeons are considered stable.