BFL-Guard: A Blockchain-Enabled Federated Learning Framework with Zero-Knowledge Gradient Verification and Tokenized Incentives

Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.

Blockchain, Federated Learning, Byzantine Fault Tolerance, Zero-Knowledge Proofs, Smart Contracts, Incentive Mechanism, Decentralized AI, IPFS, zk-SNARK

1. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. AISTATS 2017. [Google Scholar] [Crossref]

2. Blanchard, P., El Mhamdi, E.M., Guerraoui, R., & Stainer, J. (2017). Machine learning with adversaries: Byzantine tolerant gradient descent. NeurIPS 2017. [Google Scholar] [Crossref]

3. Yin, D., Chen, Y., Kannan, R., & Bartlett, P. (2018). Byzantine-robust distributed learning: Towards optimal statistical rates. ICML 2018. [Google Scholar] [Crossref]

4. Bao, X., Su, C., Xiong, Y., Huang, W., & Hu, Y. (2019). FLChain: A blockchain for auditable federated learning with trust and incentive. IEEE BigDataService 2019. [Google Scholar] [Crossref]

5. Kim, H., Park, J., Bennis, M., & Kim, S.L. (2020). Blockchained on-device federated learning. IEEE Communications Letters, 24(6), 1279–1283. [Google Scholar] [Crossref]

6. Nguyen, D.C., Ding, M., Pathirana, P.N., et al. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622–1658. [Google Scholar] [Crossref]

7. Garg, S., Gentry, C., Halevi, S., & Raykova, M. (2023). Verifiable neural network inference using zk-SNARKs. IEEE S&P 2023. [Google Scholar] [Crossref]

8. Sun, G., Cong, Y., Dong, J., et al. (2022). Data poisoning attacks on federated machine learning. IEEE Internet of Things Journal, 9(13), 11365–11375. [Google Scholar] [Crossref]

9. Fang, M., Cao, X., Jia, J., & Gong, N.Z. (2020). Local model poisoning attacks to Byzantine-robust federated learning. USENIX Security 2020. [Google Scholar] [Crossref]

10. Groth, J. (2016). On the size of pairing-based non-interactive arguments. EUROCRYPT 2016. [Google Scholar] [Crossref]

11. Ethereum Improvement Proposal 4844: Shard Blob Transactions. https://eips.ethereum.org/EIPS/eip-4844 (2023). [Google Scholar] [Crossref]

12. Wood, G. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper. [Google Scholar] [Crossref]

13. Osborne, M.J., & Rubinstein, A. (1994). A Course in Game Theory. MIT Press. [Google Scholar] [Crossref]

14. Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., & Guo, S. (2022). A Survey of Incentive Mechanism Design for Federated Learning. IEEE Transactions on Emerging Topics in Computing, 10(2), 1035–1044. [Google Scholar] [Crossref]

• Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
• Improving Sliding Mode Control with Chattering Reduction using Fuzzy Based Technique
• Cooking Quality, Fasting Blood Glucose, Glycemic Index and Load of High–Fiber Noodles Made from Wheat, Tiger Nut Residue and Cassava Flour Blends
• Matrix Rhythm Therapy Versus Interferential Therapy Combined with Lumbar Stabilization Exercises in Chronic Non-Specific Low Back Pain: A Randomized Comparative Trial
• Formulation and Sensory Evaluation of Functional Cake Prepared from Sweet Potato Powder