The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning. Alongside growing privacy-security concerns in sensitive fields, these developments have positioned federated learning (FL) as a pivotal technology for decentralized model training. Despite its vast potential, specially in the age of complex foundation models, FL encounters challenges such as elevated communication costs, computational constraints, and the complexities of non-IID data distributions. We introduce AutoFLIP, an innovative approach that utilizes a federated loss exploration phase to drive adaptive hybrid pruning, operating in a structured and unstructured way. This innovative mechanism automatically identifies and prunes model substructure by distilling knowledge on model gradients behavior across different non-IID client losses topology, thereby optimizing computational efficiency and enhancing model performance on resource-constrained scenarios. Extensive experiments on various datasets and FL tasks reveal that AutoFLIP not only efficiently accelerates global convergence, but also achieves superior accuracy and robustness compared to traditional methods. On average, AutoFLIP reduces computational overhead by 48.8% and communication costs by 35.5%, while improving global accuracy. By significantly reducing these overheads, AutoFLIP offer the way for efficient FL deployment in real-world applications for a scalable and broad applicability.