As a distributed learning paradigm, federated learning can be effectively applied to the decentralized system since it can resolve the “data island” problem. However, it is also vulnerable to serious privacy breaches. Although existing secure aggregation technique can address privacy concerns, they also incur significant additional computation and communication costs. To address these challenges, this paper offers a ommunication fficient ecure ggregation scheme. Firstly, the central server uses the communication delay between terminals as the weight of the fully terminal-connected graph to transform it into a sparse connected graph based on the minimal spanning tree. Secondly, instead of relying on central server for key advertisement, the terminals advertise keys via a neighboring terminal forwarding approach based on sparsely graph. Thirdly, we propose using the central server for auxiliary advertising to address unexpected terminal dropout. Simultaneously, we theoretically demonstrate our scheme’s security and have lower computation and communication costs. Experiments show that CESA can reduce the running time by 28.2% without sacrificing security and model accuracy compared to conventional secure aggregation when there are 10 terminals in the system.

中文翻译：

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CESA：联邦学习中通过稀疏图实现通信高效的安全聚合方案


联邦学习作为一种分布式学习范式，可以有效地应用于去中心化系统，解决“数据孤岛”问题。然而，它也容易遭受严重的隐私侵犯。尽管现有的安全聚合技术可以解决隐私问题，但它们也会带来大量的额外计算和通信成本。为了应对这些挑战，本文提供了一种通信高效的安全聚合方案。首先，中心服务器利用终端之间的通信延迟作为终端全连通图的权重，将其转化为基于最小生成树的稀疏连通图。其次，终端不依赖中央服务器进行密钥通告，而是通过基于稀疏图的邻近终端转发方法来通告密钥。第三，我们建议使用中央服务器进行辅助广告，以解决终端意外掉线的问题。同时，我们从理论上证明了我们的方案的安全性，并且具有较低的计算和通信成本。实验表明，当系统中有10个终端时，CESA在不牺牲安全性和模型精度的情况下，与传统的安全聚合相比，可以减少28.2%的运行时间。