Future wireless edge networks consist of multiple ISAC devices and various sensing targets. Federated learning (FL) is applicable to edge networks as it can integrate the sensing capabilities of multiple ISAC d
FL概念的一种描述如下图所示。 许多重要的方面使得FL区别于现存的分布式学习(Distributed Learning)方案,其中一个就是:分布式学习方案都是各学习者的数据都服从IID的随机变量的实现;FL环境下,不同的学习者可能会观察过程的不同部分(它们之间可能有重叠),因此生成的数据不能代表所有数据的分布,本地学习者的数据是Non-...
Federated Learning in Wireless Networks Mingzhe Chen, H. Vincent Poor, Walid Saad, and Shuguang Cui, "Wireless Communications for Collaborative Federated Learning," Aug. 2020.https://arxiv.org/abs/2006.02499 JihongPark, SumuduSamarakoon, Anis Elgabli, Joongheon Kim, Mehdi Bennis, Seong-Lyun Kim,...
Scheduling policies for federated learning in wireless networks IEEE Trans. Commun., 68 (1) (2020), pp. 317-333, 10.1109/TCOMM.2019.2944169 View in ScopusGoogle Scholar [131] C.T. Dinh, N.H. Tran, M.N.H. Nguyen, C.S. Hong, W. Bao, A.Y. Zomaya, V. Gramoli Federated learning...
Chen M, Gündüz D, Huang K, Saad W, Bennis M, Feljan AV, Vincent PH (2021) Distributed learning in wireless networks: recent progress and future challenges. IEEE J Sel Areas Commun J-SAC 39(12):3579–3605 MATH Google Scholar Chen P-Y, Zhang H, Sharma Y, Yi J, Hsieh C-J (20...
Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Onlin...
《40,An incentive mechanism for federated learning in wireless cellular network: An auction approach,》将基站和客户端之间的激励机制设计为一个拍卖博弈,其中基站是拍卖者,客户端是卖方。客户端希望能量消耗小,基站希望社会福利最大化。提出了一种原始-对偶贪心算法来解决NP难问题。
Federated Learning in Mobile Edge Networks: A Comprehensive Survey Base Station Association: 在密集的网络中,优化Base Station Association以限制用户所面临的干扰是很重要的。 存在中断(BIPs)事件可能是信息传输延迟的结果,当用户的身体运动阻碍了无线链路时,就会造成这种延迟。 现有的方法存在的问题: traditional clo...
2.1. Federated learning for wireless networks: applications and protocols The adoption of FL in wireless networks has recently gained a significant momentum. In a recent review, Niknam et al.[22]discussed some keypotential applicationsof FL in5G networks: for example, FL may play a crucial role...
5.3 Federated learning Federated learning is one of the hot topics in the context of machine learning. With the wake of 5G and B5G networks, different devices will use the new mobile network communications for which low latency, high data rates, and massive and intensive connectivity are signifi...