联邦脑电训练怕泄露？这篇用掩码安全聚合在Flower上实现，TUH EEG验证，半诚实开销最低。
论文提出一个基于掩码安全聚合的联邦学习框架，用于TUH EEG临床数据。框架结合图通信、阈值秘密共享、抗掉线聚合、更新裁剪、布隆过滤器记录链接和公证人验证，并用Flower实现。在TUH EEG衍生数据的跨孤岛模拟中，安全变体对聚合服务器隐藏个体更新，且与联邦模型训练兼容。在Flower的多种客户端配置下，半诚实变体开销最低，恶意设置变体和辅助公证人变体提供更强一致性但增加计算、通信和轮时开销。
Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privacy leakage from individual model updates. This paper presents a privacy-preserving federated learning framework for clinical EEG data using masking-based secure aggregation as the core protection mechanism. The framework combines graph-based communication, threshold secret sharing, dropout-resilient aggregation, local update clipping, an optional Bloom filter-based privacy-preserving record-linkage initialization module, and auxiliary-notary-based verifiability. It supports both semi-honest and malicious aggregation settings and is implemented using the Flower federated learning framework. The secure-aggregation variants are evaluated in a simulated cross-silo healthcare setting using TUH EEG-derived data under different client configurations. Under the stated assumptions, the secure variants hide individual updates from the aggregation server. The results show that these variants remain compatible with federated model training, although malicious-setting safeguards and lightweight consistency-checking mechanisms introduce additional computation, communication, and round-duration overhead. The semi-honest variant provides the lowest overhead among the secure configurations, while malicious and auxiliary-notary variants offer stronger consistency, integrity, and lightweight verification support at higher cost.