  With the rapid development of machine learning and a growing concern for data
privacy, federated learning has become a focal point of attention. However,
attacks on model parameters and a lack of incentive mechanisms hinder the
effectiveness of federated learning. Therefore, we propose A Privacy Protected
Blockchain-based Federated Learning Model (PPBFL) to enhance the security of
federated learning and encourage active participation of nodes in model
training. Blockchain technology ensures the integrity of model parameters
stored in the InterPlanetary File System (IPFS), providing protection against
tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW)
consensus algorithm tailored for federated learning, aiming to incentive
training nodes. This algorithm rewards nodes with greater computational power,
promoting increased participation and effort in the federated learning process.
A novel adaptive differential privacy algorithm is simultaneously applied to
local and global models. This safeguards the privacy of local data at training
clients, preventing malicious nodes from launching inference attacks.
Additionally, it enhances the security of the global model, preventing
potential security degradation resulting from the combination of numerous local
models. The possibility of security degradation is derived from the composition
theorem. By introducing reverse noise in the global model, a zero-bias estimate
of differential privacy noise between local and global models is achieved.
Furthermore, we propose a new mix transactions mechanism utilizing ring
signature technology to better protect the identity privacy of local training
clients. Security analysis and experimental results demonstrate that PPBFL,
compared to baseline methods, not only exhibits superior model performance but
also achieves higher security.
