Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy. However, existing HHE-FL systems rely on a single homomorphic key pair shared across all clients, which forces them to assume an unrealistically weak threat model: if a client misbehaves or intercepts another's traffic, private updates can be exposed. We eliminate this weakness by integrating two alternative key protection mechanisms into the HHE-FL workflow. The first is masking, where client keys are blinded before homomorphic encryption and later unblinded homomorphically by the server. The second is RSA encapsulation, where homomorphically encrypted keys are additionally wrapped under the server's RSA public key. These countermeasures prevent key misuse by other clients and extend HHE-FL security to adversarial settings with malicious participants. We implement both approaches on top of the Flower framework using the PASTA/BFV HHE scheme and evaluate them on the MNIST dataset with 12 clients. Results show that both mechanisms preserve model accuracy while adding minimal overhead: masking incurs negligible cost, and RSA encapsulation introduces only modest runtime and communication overhead.

翻译：联邦学习（FL）支持在保持敏感数据位于客户端设备的前提下进行协同训练，但本地模型更新仍可能泄露隐私信息。混合同态加密（HHE）已被应用于FL以减轻客户端开销并保护隐私。然而，现有HHE-FL系统依赖所有客户端共享单个同态密钥对，这迫使其假设不切实际的弱威胁模型：若客户端行为异常或截获其他客户端流量，私有更新将面临泄露风险。我们通过将两种替代密钥保护机制集成至HHE-FL工作流来消除此缺陷。第一种是掩码机制，客户端密钥在同态加密前进行盲化，并由服务器以同态方式去盲。第二种是RSA封装机制，将同态加密的密钥额外包裹在服务器的RSA公钥下。这些防护措施可防止其他客户端滥用密钥，并将HHE-FL安全性扩展至包含恶意参与者的对抗性场景。我们基于Flower框架，采用PASTA/BFV HHE方案实现这两种方法，并在包含12个客户端的MNIST数据集上评估。结果表明两种机制在保持模型精度的同时仅引入微小开销：掩码机制成本可忽略，RSA封装仅带来适度的运行时及通信开销。