Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process or compromising the entire model. In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture. This is a feature lacking in the initial client selection and deployment mechanism of the On-Demand architecture. We introduce a trust mechanism, named "Trusted-On-Demand-FL", which establishes a relationship of trust between the server and the pool of eligible clients. Utilizing Docker in our deployment strategy enables us to monitor and validate participant actions effectively, ensuring strict adherence to agreed-upon protocols while strengthening defenses against unauthorized data access or tampering. Our simulations rely on a continuous user behavior dataset, deploying an optimization model powered by a genetic algorithm to efficiently select clients for participation. By assigning trust values to individual clients and dynamically adjusting these values, combined with penalizing malicious clients through decreased trust scores, our proposed framework identifies and isolates harmful clients. This approach not only reduces disruptions to regular rounds but also minimizes instances of round dismissal, Consequently enhancing both system stability and security.

翻译：容器化技术在联邦学习（FL）配置中发挥着关键作用，它扩展了潜在客户端的规模，并确保每个学习轮次所需特定子集的可用性。然而，在FL场景中，作为客户端部署的设备可信度存在疑问，尤其是在涉及容器部署流程时。解决这些挑战至关重要，尤其是在管理可能破坏学习过程或危及整个模型的恶意客户端时。我们的研究旨在将信任元素集成到系统架构中的客户端选择与模型部署流程中。这是按需架构初始客户端选择与部署机制所缺失的功能。我们提出了一种名为"Trusted-On-Demand-FL"的信任机制，该机制在服务器与符合条件的客户端池之间建立了信任关系。在部署策略中利用Docker，使我们能够有效监控和验证参与者行为，确保严格遵守商定协议，同时加强对未经授权数据访问或篡改的防御。仿真实验基于连续的用户行为数据集，采用遗传算法驱动的优化模型高效选择参与客户端。通过为单个客户端分配信任值并动态调整这些值，同时通过降低信任评分来惩罚恶意客户端，所提出的框架能够识别并隔离有害客户端。该方法不仅减少了对正常轮次的干扰，还最大限度地减少了轮次中止事件的发生，从而增强了系统稳定性与安全性。