Recently, the trending 5G technology encourages extensive applications of on-device machine learning, which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device’s trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain’s decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain, we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism, which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy.

最近，5G 技术的发展趋势鼓励了终端机器学习的广泛应用，该机器学习收集用户数据用于模型训练。这需要具有成本效益的技术来保护资源受限环境中模型训练的隐私和安全性。传统的学习方法依赖于系统之间对隐私和安全的信任。然而，随着学习规模的增加，维护每个边缘设备的可信度可能会很昂贵。为了在去信任的环境中经济有效地建立信任，本文提出了民主学习 (DemL)，这是探索区块链安全分散式设备学习的硬件/软件协同设计的第一步。通过利用区块链的去中心化和防篡改，我们的设计可确保在去信任环境中进行 AI 学习。为了解决区块链带来的额外开销，我们提出了 PoMC（一种算法和架构协同设计）作为一种新颖的区块链共识机制，它首先利用了 AI 学习架构中的跨域重用（AI 学习和区块链共识）。评估结果表明，我们的 DemL 可以保护 AI 学习免受隐私泄露和模型污染，并证明隐私和安全性伴随着微不足道的硬件开销和功耗（2%）。我们相信，我们的工作将为协同区块链和设备学习以实现安全和隐私打开大门。