# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
## 6 Conclusions  
This paper presents pFeBASC, a novel blockchain-assisted semi-centralized pFL framework designed to improve accuracy and efficiency in real-world environments, particularly suited to the distributed data landscapes of IoT. By integrating both centralized and decentralized FL frameworks, pFeBASC effectively leverages network connections within IoT to facilitate FL tasks while decentralizing aggregation operations across clients to minimize communication overhead. The framework not only involves meticulous system modeling and formal process descriptions but also includes the design of blockchain block structures--_download blocks, upload blocks_, and _evaluation blocks_--that guide the deployment of BFL systems. Our extensive experiments demonstrate pFeBASC's excellent accuracy, efficiency, and robustness to imbalance rates. Embedding FedHype into pFeBASC, which utilizes small local HNs for client-side knowledge exchange, addresses the challenges of pFL training and data heterogeneity by training personalized target networks through local HN aggregation. This innovative approach not only alleviates issues of data heterogeneity but also conserves communication costs. Moreover, HNs enable collaborative training among clients with heterogeneous models by generating parameters for TNs with varying architectures.  
While our experiments have validated the effectiveness of the method, our next focus will be on providing theoretical proof of algorithm convergence for the semi-centralized framework. In pFeBASC, the initial design did not address FL tasks regarding blockchain consensus mechanisms effectively, and evaluation blocks, despite being designed, were not utilized. Future work will refine the integration of the blockchain algorithm design and FL. Additionally, since HNs can generate parameters for TNs with varying architectures, this supports collaborative training among clients with heterogeneous models. Moving forward, we plan to explore whether pFeBASC can maintain satisfactory model accuracy in heterogeneous model FL scenarios.  
Conceptualization and methodology, Y.Z. and H.X.; data curation, X.P.; writing--original draft preparation, Y.Z.; writing--review and editing, H.X. and X.P.; supervision, H.X. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the National Natural Science Foundation of China under Grant 62102212.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MNIST**} & \multicolumn{2}{c}{**FMNIST**} & \multicolumn{2}{c}{**CIFAR10**} & \multicolumn{2}{c}{**CIFAR100**} \\ \cline{2-9}  & **non-iid** & **iid** & **non-iid** & **iid** & **non-iid** & **iid** & **non-iid** & **iid** \\ \hline FedHype + FedAvg & \(-1.22\%\) & \(-0.9\%\) & \(1.18\%\) & \(1.13\%\) & \(10.75\%\) & \(12.33\%\) & \(10.53\%\) & \(10.94\%\) \\ FedHype + FedProx & \(-1.09\%\) & \(-0.66\%\) & \(0.3\%\) & \(0.82\%\) & \(6.54\%\) & \(13.65\%\) & \(12.95\%\) & \(10.06\%\) \\ FedHype + FedDyn & \(-0.98\%\) & \(-0.66\%\) & \(-0.41\%\) & \(0.98\%\) & \(7.47\%\) & \(13.16\%\) & \(14.58\%\) & \(9.39\%\) \\ FedHype + FedBN & \(-1.03\%\) & \(-1.55\%\) & \(-1.07\%\) & \(0.98\%\) & \(0.13\%\) & \(29.35\%\) & \(3.67\%\) & \(4.25\%\) \\ FedHype + pFedLa & \(-0.46\%\) & \(-0.25\%\) & \(0.26\%\) & \(2.51\%\) & \(8.76\%\) & \(16.18\%\) & \(13.24\%\) & \(15.62\%\) \\ FedHype + FedFomo & \(\mathbf{4.74\%}\) & \(5.25\%\) & \(7.8\%\) & \(12.44\%\) & \(13.83\%\) & \(40.25\%\) & \(16.7\%\) & \(22.88\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy variation when FedHype is combined with other baseline algorithms.