Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles - LIU Miao; LIN Wanru; WANG Qin; GUI Guan (Tongxin xuebao)
In Internet of vehicles (IoV) scenario, there was a massive amount of non-independent and identically distributed data among devices, leading to data heterogeneity problems of federated learning (FL). This problem affected the performances of model training and might pose threats to traffic safety. Therefore, the focus lied on the data heterogeneity problem of FL in IoV, the personalized solution system and new research ideas were proposed through problem attribution. Firstly, the necessity of applying FL to IoV was discussed. Through an examination of current applications, identified the data heterogeneity problems of FL in IoV. Secondly, classified and traced the data heterogeneity problems of FL in IoV, from the perspective of perception, computation, and transmission respectively. Thirdly, personalized methods were introduced as the core approaches to address the data heterogeneity problems of FL in IoV, and analyzed the advantages and disadvantages of existing personalized federated learning (PFL). Finally, the challenges encountered by PFL in IoV were outlined, along with the future research prospection related to advanced technologies on wireless communications.