Such requirements fundamentally conflict with the characteristics of wireless edge environments, which are distributed, heterogeneous, resource-constrained, latency-sensitive, and dynamically evolving. This Special Issue (SI) seeks high-quality original research and comprehensive surveys addressing the theories, architectures, algorithms, systems, and applications of distributed intelligence and collaborative inference for foundation models over wireless edge networks. Topics of interest include, but are not limited to:Distributed learning and adaptation of foundation models over wireless edge networks. Federated learning, split learning, decentralized learning, and continual learning for foundation models. Wireless foundation models and domain-specific foundation models.