With the continues increasing importance of data privacy， fine-tuning Pre-trained Foundational Model （PFM） for downstream tasks has become increasingly challenging， leading to the emergence of federated learning research based on PFM. However， PFM poses significant challenges to federated learning systems， especially in terms of local computation and communication. Therefore， the corresponding solution schemes were proposed for the two main stages of federated learning： local computing and aggregation communication， namely the local efficient fine-tuning mode and the ring-shaped local aggregation mode. In the first mode， a model pruning algorithm based on Parameter-Efficient Fine-Tuning （PEFT） was employed to reduce local computation and communication costs. In the second mode， the centralized aggregation method was replaced with a distributed local aggregation scheme to enhance communication efficiency during the aggregation stage. Experimental results demonstrate that the proposed federated parameter-efficient fine-tuning framework for large model performs well in terms of both final performance and efficiency.

Focused on the current lack of fair and reasonable incentive mechanism for federated learning， and the difficulty in measuring the contribution to federated learning by participant nodes with different data volumes， different data qualities， and different data distributions， a new incentive mechanism for federated learning based on Generative Adversarial Network （GAN） was proposed. Firstly， a GAN with Trained model （GANT） was proposed to achieve high-precision sample generation. Then， the contribution evaluation algorithm of the incentive mechanism was implemented based on GANT. The algorithm filtered samples and generated data labels through the joint model， and introduced the local data labels of the participant nodes to balance the impact of non-independent identically distributed data labels on the contribution evaluation. Finally， a two-stage Stackelberg game was used to realize the federated learning incentive process. The security analysis results show that the proposed incentive mechanism ensures data security and system stability in the process of federated learning. The experimental results show that the proposed incentive mechanism is correct， and the contribution evaluation algorithm has good performance under different data volumes， different data qualities and different data distributions.

Focusing on coarse granularity of access control， low sharing flexibility and security risks such as data leakage of centralized medical data sharing platform， a blockchain-based hierarchical access control and sharing system of medical data was proposed. Firstly， medical data was classified according to sensitivity， and a Ciphertext-Policy Attribute-Based Hierarchical Encryption （CP-ABHE） algorithm was proposed to achieve access control of medical data with different sensitivity. In the algorithm， access control trees were merged and symmetric encryption methods were combinined to improve the performance of Ciphertext-Policy Attribute-Based Encryption （CP-ABE） algorithm， and the multi-authority center was used to solve the key escrow problem. Then， the medical data sharing mode based on permissioned blockchain was used to solve the centralized trust problem of centralized sharing platform. Security analysis shows that the proposed system ensures the security of data during the data sharing process， and can resist user collusion attacks and authority collusion attacks. Experimental results also show that the proposed CP-ABHE algorithm has lower computational cost than CP-ABE algorithm， the maximum average delay of the proposed system is 7.8 s， and the maximum throughput is 236 transactions per second， which meets the expected performance requirements.