## 4 Proposed Protocol
### Reputation Management
The results demonstrate that the proposed protocol successfully mitigates the impact of malicious actors by using voter credibility to determine their trustworthiness, and the reputation-aware reward system is an attractive incentive for participants. The protocol was also tested in a scenario where malicious participants started with higher initial reputations; it still effectively reduced the number of malicious participants with subsequent iterations. Future research can focus on measuring the response time of a blockchain-integrated FL scheme with a voting mechanism to determine its scalability. Additionally, since the paper defines that voters vote on all trainers, it can be useful to develop a worker selection algorithm to determine the assignment pairings of trainers and voters. Conceptualization, B.S. ; data curation, B.S. ; formal analysis, B.S. ; funding acquisition, S.-G.L. ; investigation, B.S. ; methodology, B.S. ; project administration, S.-G.L. ; resources, S.-G.L. ; supervision, S.-G.L. ; validation, B.S. and S.-G.L. ; visualization, B.S. ; Writing--original draft, B.S. ; writing--review and editing, B.S., S.-G.L. and E.N.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2018R1D1A1B07047601). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Advances and Open Problems in Federated Learning. _arXiv_**2021**, arXiv:1912.04977. * (2) [PERSON]; [PERSON]; [PERSON] Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 22 nd ACM SIGSAC Conference on Computer and Communications Security, CCS '15, New York, NY, USA, 12-16 October 2015; pp. 1322-1333. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON] Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning. _arXiv_**2018**, arXiv:1807.06629. * (4) [PERSON]; [PERSON] FLAD: Adaptive federated learning for DDoS attack detection. _arXiv_**2022**, arXiv:2205.06661. * (5) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus. _IEEE Netw._**2021**, _35_, 234-241. [CrossRef]
* (6) [PERSON]; [PERSON]; [PERSON] BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning. _arXiv_**2022**, arXiv:2202.02817. * (7) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices. _arXiv_**2021**, arXiv:1906.10893. * (8) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Byzantine Resistant Secure Blockchained Federated Learning at the Edge. _IEEE Netw._**2021**, _35_, 295-301. [CrossRef]
* (9) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting. _arXiv_**2022**, arXiv:2207.07779. * (10) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Chained anomaly detection models for federated learning: An intrusion detection case study. _Appl. Sci._**2018**, \(8\), 2663. [CrossRef]
* (11) [PERSON]; [PERSON]; [PERSON] [PERSON] Towards a Secure and Reliable Federated Learning using Blockchain. _arXiv_**2022**, arXiv:2201.11311. * (12) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive. _IEEE Trans. Dependable Secur. Comput._**2019**, _18_, 2438-2455.