联邦学习安全聚合新方案：支持异构量化精度
Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization
做联邦学习隐私的可以看看，这个协议允许各客户端用不同量化精度上传，还能抗合谋和掉线。
该论文面向联邦学习中的安全聚合问题，允许 N 个客户端使用不同的量化精度上传模型更新。现有异构量化方案在客户端掉线或与服务器合谋时会泄露部分聚合值甚至单个更新片段。作者提出基于 (T+1, N) 门限黑盒秘密共享的协议，可容忍客户端掉线和最多 T 个半诚实客户端与服务器合谋。不同量化精度产生的不同代数群份额通过相同的线性整数系统生成。
Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization
In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients, without revealing any information on the individual model updates to the server, beyond their sum. Different clients may be subject to heterogeneous bandwidth constraints, and consequently may choose different numbers of quantization levels to represent the information they send. Most secure aggregation works require the clients to project the values of their update parameters to a common finite group (or field), which does not directly accommodate heterogeneous precision constraints on their transmitted masked updates. Existing approaches of secure aggregation with heterogeneous quantization levels reveal partial aggregates of model-updates, and can reveal segments of individual model updates, under certain dropout and client-server collusion patterns. To prevent this privacy breach, we propose an information-theoretic secure aggregation scheme that tolerates client drop-outs, and up to $T$ semi-honest clients that collude with each other and with the server, under heterogeneous quantization at clients. Our protocol utilizes the $(T+1, N)$ threshold black-box secret sharing construction, where secret shares of the client updates, despite belonging to distinct algebraic groups owing to different quantization, are generated from identical linear integer systems.