Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the server can infer sensitive client data from trained models. Google's Secure Aggregation (SecAgg) protocol addresses this data privacy issue by masking each client's trained model using shared secrets and individual elements generated locally on the client's device. Although SecAgg effectively preserves privacy, it imposes considerable communication and computation overhead, especially as network size increases. Building upon SecAgg, this poster introduces a Communication-Efficient Secure Aggregation (CESA) protocol that substantially reduces this overhead by using only two shared secrets per client to mask the model. We propose our method for stable networks with low delay variation and limited client dropouts. CESA is independent of the data distribution and network size (for higher than 6 nodes), preventing the honest-but-curious server from accessing unmasked models. Our initial evaluation reveals that CESA significantly reduces the communication cost compared to SecAgg.

翻译：联邦学习（FL）是一种去中心化的机器学习方法，客户端设备在本地训练模型，并将其发送给执行聚合以生成全局模型的服务器。FL容易受到模型反转攻击，即服务器可能从训练模型中推断出敏感的客户端数据。谷歌的安全聚合（SecAgg）协议通过利用在客户端设备本地生成的共享秘密和独立元素来掩蔽每个客户端的训练模型，从而解决了这一数据隐私问题。尽管SecAgg有效保护了隐私，但它带来了相当大的通信和计算开销，尤其是随着网络规模的扩大。基于SecAgg，本海报介绍了一种通信高效的安全聚合（CESA）协议，该协议通过为每个客户端仅使用两个共享秘密来掩蔽模型，从而大幅降低了开销。我们针对延迟变化小且客户端退出率有限的稳定网络提出了该方法。CESA与数据分布和网络规模（节点数超过6个时）无关，可防止诚实但好奇的服务器访问未掩蔽的模型。初步评估表明，与SecAgg相比，CESA显著降低了通信成本。