FEDERATED LEARNING WITH BLOCKCHAIN FOR PRIVACY PRESERVING AI

Abstract

In this paper, we introduce a privacy-conscious federated learning (FL) system that builds upon a blockchain-based coordination layer to offer provable provenance and incentive alignment without revealing the underlying data. The stacking of differential privacy (DP), secure aggregation (SA), and smart contracts, which handle registration, commitreveal logging, challenges and payouts, are stacked. We test the methodology and apply it to image (CIFAR-10, FEMNIST), text (sentiment), and tabular tasks with non-IID partitions and adversarial setting. Relative to plain FL, the full stack lags behind by approximately 1.5- 2.0 accuracy points on the average, most of the loss to DP and not the ledger. DP obtains ε=6.3 ( 10-5) and reduces membership-inference AUC significantly, whereas robust aggregation combined with DP and SA decreases backdoor success by 62 to 5.8 at 20 percent malicious clients. Consensus based on permissioned BFT makes this difference of an additional ca. 0.35 s/round; on public PoS networks, Layer-2 anchoring and micro-batching reduces confirmation latency by an order of magnitude with insignificant utility cost.

Keywords

Federated learning, blockchain, differential privacy, secure aggregation, smart contracts, auditability, incentive mechanisms, PBFT, Proof-of-Stake, Layer-2 rollups, non-IID data, robustness, membership inference, backdoor attacks, scalability.