EigenFL: An EigenLayer-Restaked Blockchain Solution for Secure Federated Learning

Abstract

Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping data decentralized. However, traditional FL architectures suffer from critical trust issues, including vulnerability to model poisoning attacks, a lack of incentives for honest participation, and the inability to verify the quality of submitted model updates. Blockchain-based solutions have been proposed to address these challenges, but they often require participants to bootstrap independent validator networks and lock additional capital, limiting scalability and practical adoption. This paper proposes EigenFL, a novel FL framework secured through blockchain restaking, leveraging EigenLayer to provide cryptoeconomic security and decentralized validation by reusing Ethereum's existing validator infrastructure. Participants are economically incentivized to submit honest updates, while malicious behavior is discouraged through slashing mechanisms. Model updates are validated off-chain by independent operators, and their outcomes are immutably recorded on-chain, ensuring accountability and robustness. EigenFL demonstrates how blockchain restaking can transform FL from a trust-based paradigm into a cryptoeconomically secured framework, enabling verifiable and decentralized training.