# Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
## 5 Conclusions  
This paper culminates with an in-depth discussion of the enhanced scheme for FL, which harnesses the power of blockchain technology. The scheme eliminates the need for a dedicated server and a public bulletin board in the EIFFeL scheme, thereby reducing the risks associated with the compromise of these entities. The clients share credentials and intermediary results on the blockchain platform, allowing them to verify the validity of credentials and results from other users. This paper provides a performance analysis of the enhanced scheme, demonstrating its efficiency and effectiveness in the realm of FL. This paper shows that blockchain technology can enhance the privacy and integrity of FL and open up new possibilities for collaborative ML in various domains.  
There are several directions for future work. Here, we only mention two examples. One is to fully implement the proposed scheme, particularly by selecting a blockchain platform, and encoding the desired operations into smart contracts. This will help us understand the overall complexity of the proposed scheme. The other one is to incorporate the concept of differential privacy to further reduce the privacy risks. To this end, it  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Category** & **Number of Records** \\ \hline Benign & 477,737 \\ ICMPFood & 1155 \\ HTTP Flood & 140,812 \\ SlowrateDos & 73,124 \\ SYNFolod & 9721 \\ SYNScan & 20,043 \\ TCPConnectScan & 20,052 \\ UDPFood & 457,340 \\ UDPScan & 15,906 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 5G-NIDD dataset attack categories and distribution.  
\begin{table}
\begin{tabular}{l l r r r r} \hline \hline \multirow{2}{*}{**Step**} & \multirow{2}{*}{**Participant**} & \multicolumn{4}{c}{**Runtime (ms)**} \\ \cline{3-6}  & & **n = 50** & **100** & **150** & **200** \\ \hline \multirow{2}{*}{2.(a.)} & Client & 491.37 & 974.21 & 1477.88 & 1990.13 \\  & Blockchain & - & - & - & - \\  & Client & 1073.43 & 2106.11 & 3204.75 & 4283.49 \\  & Blockchain & 995.14 & 1997.64 & 3091.71 & 4107.93 \\  & Client & 2079.58 & 4215.38 & 6314.49 & 8284.27 \\  & Blockchain & - & - & - & - \\
3. & Client & 171.21 & 354.14 & 498.87 & 637.61 \\  & Blockchain & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Runtime of proposed scheme in ms.  
is interesting to see how privacy enhancement could affect accuracy and other metrics. This will help us understand the precise trade-offs between privacy protection and the performance of FL.  
Conceptualization, Q.T.; formal analysis, B.L. and Q.T.; funding acquisition, B.L. and Q.T.; investigation, B.L. and Q.T.; methodology, B.L. and Q.T.; project administration, Q.T.; software, B.L.; supervision, Q.T.; validation, B.L. and Q.T.; writing--original draft, B.L. and Q.T.; writing--review and editing, B.L. and Q.T. All authors have read and agreed to the published version of the manuscript.  
[PERSON] and [PERSON] are supported by the 5G-INSIGHT bi-lateral project (ANR-20-CE25-0015-16), funded by the Luxembourg National Research Fund (FNR) and the French National Research Agency (ANR).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.