In federated learning (FL), data providers jointly train a model without disclosing their training data. Despite its privacy benefits, a malicious data provider can simply deviate from the correct training protocol without being detected, thus attacking the trained model. While current solutions have explored the use of trusted execution environment (TEEs) to combat such attacks, there is a mismatch with the security needs of FL: TEEs offer confidentiality guarantees, which are unnecessary for FL and make them vulnerable to side-channel attacks, and focus on coarse-grained attestation, which does not capture the execution of FL training. We describe ExclaveFL, an FL platform that achieves end-to-end transparency and integrity for detecting attacks. ExclaveFL achieves this by employing a new hardware security abstraction, exclaves, which focus on integrity-only guarantees. ExclaveFL uses exclaves to protect the execution of FL tasks, while generating signed statements containing fine-grained, hardware-based attestation reports of task execution at runtime. ExclaveFL then enables auditing using these statements to construct an attested dataflow graph and then check that the FL training jobs satisfies claims, such as the absence of attacks. Our experiments show that ExclaveFL introduces a less than 9% overhead while detecting a wide-range of attacks.

翻译：在联邦学习（FL）中，数据提供方在不公开其训练数据的情况下联合训练模型。尽管具有隐私优势，恶意数据提供方可以轻易偏离正确的训练协议而不被检测，从而攻击训练后的模型。虽然现有解决方案已探索使用可信执行环境（TEE）来应对此类攻击，但其与联邦学习的安全需求存在错配：TEE提供机密性保证（这对联邦学习并非必需且使其易受侧信道攻击），并侧重于粗粒度的证明，无法捕捉联邦学习训练的实际执行过程。本文提出ExclaveFL，一个通过实现端到端透明性与完整性来检测攻击的联邦学习平台。ExclaveFL通过采用新型硬件安全抽象——飞地（exclaves）实现这一目标，该抽象专注于纯完整性保证。ExclaveFL利用飞地保护联邦学习任务的执行，同时生成包含运行时任务执行的细粒度硬件证明报告的签名声明。随后，ExclaveFL支持基于这些声明进行审计，构建可验证的数据流图，进而检验联邦学习训练任务是否满足无攻击等声明。实验表明，ExclaveFL在检测多种攻击的同时仅引入低于9%的性能开销。