Trusted Execution Environments (TEEs)-aided federated learning protocols emerge as promising solutions to counter server-side adversaries and ensure the trustworthiness of the server. In this paper, we dissect existing protocols and demonstrate that server-side adversaries can still manipulate client selection and replay aggregation to compromise system robustness and privacy, by exploiting TEE limitations, i.e., state rollback and I/O manipulation. To this end, we present DIST-FL, a distributed system of servers guarded by multiple TEEs forming an append-only ledger for privacy-preserved, robust FL aggregation. Specifically, DIST-FL ensures operation linearizability to thwart state rollback attacks and incorporates inputs from reliable servers to mitigate I/O manipulation threats. We implement DIST-FL and conduct evaluations in WAN settings. Experimental results demonstrate that DIST-FL can effectively counter the proposed attacks and match the single-TEE's performance while offering a 6x throughput boost over its counterparts, leveraging TEE's computational advantages.

翻译：可信执行环境（TEE）辅助的联邦学习协议作为应对服务器端攻击者并确保服务器可信性的有前景方案而兴起。本文剖析现有协议，并证明服务器端攻击者仍可通过利用TEE的局限性（即状态回滚与I/O操纵）操控客户端选择与重放聚合操作，从而破坏系统鲁棒性与隐私保护。为此，我们提出DIST-FL——一种由多个TEE守护的服务器分布式系统，通过构建仅追加账本实现隐私保护且鲁棒的联邦聚合。具体而言，DIST-FL通过确保操作线性化以抵御状态回滚攻击，并整合可靠服务器输入以缓解I/O操纵威胁。我们在广域网环境下实现了DIST-FL并进行评估。实验结果表明，DIST-FL能有效应对所提出的攻击，在匹配单TEE性能的同时，利用TEE的计算优势实现相比同类方法6倍的吞吐量提升。