Decentralized Federated Learning with Byzantine Fault Tolerance via Proof-of-Stake

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants intentionally submit incorrect or misleading updates, compromising the integrity and accuracy of the global model. This paper proposes a novel decentralized federated learning system that integrates Byzantine fault tolerance and a Proof-of-Stake (PoS) incentive mechanism to mitigate these vulnerabilities. The core idea is to incentivize participants to contribute honest updates while simultaneously providing cryptographic proofs to verify the validity of these updates. This system constructs a decentralized network where participants stake resources (e.g., computational power or storage) to gain voting rights and rewards for contributing correct updates. Malicious behavior is penalized through a reduction in stake and potentially exclusion from the network. The system utilizes cryptographic techniques, such as zero-knowledge proofs, to ensure the integrity of updates without revealing the underlying data. This approach significantly enhances the robustness of FL systems against adversarial attacks and promotes a more trustworthy and reliable learning environment. The proposed system addresses the critical challenge of Byzantine fault tolerance in FL, offering a practical and scalable solution for deploying FL models in sensitive environments.