This paper presents MobiFormer, a split-federated transfer learning framework with multi-head attention designed for distributed drone Radio Access Network (RAN) slicing. The objective is to optimize slice performance isolation and training costs. Based on a flexible service metric, the problem of maximizing slice performance isolation quality is formulated as a joint optimization of slice windowing and resource allocation. For single drone autonomous operations, we construct an “unconstrained mobility and sustainable fine-tuning” paradigm, enabling drones to adapt previously trained resource slicing models to new environments with the assistance of multiple target-domain terrestrial Base Stations (BSs). This adaptation is facilitated by a Source-free Multi-target-domain Transfer Learning (SMTL) approach, where Transformer-based multi-head attention is employed on drones to integrate fine-tuned models from multiple target-domain BSs. Building on SMTL and continuing its scenario, a Clustered Split Federated Learning (CSFL) approach is developed to support multi-drone collaborative training, where BSs serve as cluster heads to aggregate parameters from member drones. To save energy, part of the onboard models are migrated to BSs while local iterations occur through gradient exchanges. Unlike SMTL, the Transformer is deployed at BSs to enhance the global model's adaptability and generalization. Extensive simulations demonstrate that MobiFormer outperforms benchmark approaches in terms of performance isolation, energy consumption, and online decision-making efficiency in distributed learning settings.