Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.

中文翻译：

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用于低碳电气设备管理的云-边缘-设备协作可靠且通信高效的数字孪生


联邦学习 (FL) 是人工智能 (AI) 的新曙光，其中机器学习模型以分布式方式构建，同时在集中式聚合器和客户端医疗物联网 (IoMT) 节点之间仅通信模型参数。这种学习技术的性能可能会受到恶意干扰机器人的活动的严重阻碍。在本文中，我们从长期学习持续时间的角度研究干扰机存在下 IoMT 域中上行链路 FL 过程的客户端选择和信道分配以及功率控制问题。我们将每次学习迭代中 FL 网络和干扰器之间的交互映射为 Stackelberg 博弈，其中干扰器充当领导者，FL 网络充当跟随者。我们将客户端和渠道选择以及力量控制共同考虑作为本次博弈的策略。在制定游戏时，我们利用凸（DC）编程方法和对偶分解技术的差异，找到了两种类型玩家的联合最佳响应策略。除了双方玩家都可以获得完整的信息之外，我们还从 FL 网络知道对方玩家的部分信息的角度来研究这个问题。已经进行了广泛的模拟来验证所提出的算法在干扰游戏中的有效性。