Fed-RSAdapter: Federated Fine-Tuning of Remote Sensing Images via Multi-Scale Adapter Modules

In edge computing scenarios for remote sensing image interpretation, two fundamental challenges constrain the effectiveness of federated fine-tuning: the limited computational capacity of edge devices restricts the multi-scale feature learning capability of lightweight deployed models, while the highly heterogeneous and imbalanced data distributions (Non-IID settings) across clients render conventional parameter aggregation strategies ineffective. To address these challenges jointly, this paper proposes Fed-RSAdapter, a federated fine-tuning framework for remote sensing imagery based on multi-scale adapter modules. On the edge side, a multi-scale adapter architecture with cross-layer dense connections and feature map concatenation alignment is introduced. By inserting lightweight adapter modules in parallel within a frozen backbone, the proposed design captures multi-scale spatial characteristics inherent in high-resolution remote sensing imagery while confining trainable parameters to less than 5% of the full model, thereby satisfying strict on-device computational and communication constraints. On the server side, a parameter similarity-aware aggregation strategy is designed to handle client heterogeneity. Client adapter parameters are normalized and mapped into a similarity matrix via Gaussian kernel distance, enabling the server to perform personalized weighted aggregation that balances global generalization with client-specific adaptation. Extensive experiments on scene classification, object detection, and semantic segmentation benchmarks demonstrate that Fed-RSAdapter achieves an average improvement of 3.75% in overall accuracy for scene classification, 0.74% in mAP for object detection, and 0.25% in overall accuracy for semantic segmentation over federated baselines, while reducing the volume of transmitted parameters to approximately 4.93% of that required by conventional full-parameter federated learning. These results demonstrate the communication-efficient fine-tuning capability of the proposed framework under federated remote sensing settings. We further clarify that actual deployment on embedded edge hardware is not directly evaluated in this work and is therefore treated as an important direction for future validation rather than an experimentally proven conclusion.