Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate EncCluster's scalability across encryption levels. Our findings reveal that EncCluster significantly reduces communication costs - below even conventional FedAvg - and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances.

翻译：联邦学习（FL）通过在分散设备间仅通信本地模型更新至聚合服务器，实现跨设备的模型训练。尽管这种有限的数据共享使得FL比集中式方法更为安全，但FL在模型更新传输过程中仍易受到推理攻击。现有的安全聚合方法依赖于差分隐私或功能加密（FE）等加密方案来保护个体客户端数据。然而，此类策略可能会降低性能，或对运行在资源受限的边缘设备上的客户端带来不可接受的计算与通信开销。本研究提出EncCluster，一种创新方法，该方法通过权重聚类整合模型压缩，结合最新的去中心化FE以及使用概率过滤器的隐私增强数据编码，从而在不影响模型性能或给客户端增加不必要负担的前提下，为FL提供强大的隐私保障。我们进行了涵盖多种数据集和架构的全面评估，以证明EncCluster在不同加密级别上的可扩展性。我们的研究结果表明，EncCluster显著降低了通信成本——甚至低于传统的FedAvg——并将加密速度较所有基线提升四倍以上；同时，它保持了高模型精度和增强的隐私保障。