Mitigating Poor Data Quality Impact with Federated Unlearning for Human-Centric Metaverse

Federated Learning (FL), which has been employed to train machine learning models on the data with a distributed manner, could enhance the immersive user experience for the human-centric metaverse. However, it's challenging to train machine learning models accurately and promptly with FL for the human-centric metaverse due to massive data communication and user unreliability. User experience could be negatively affected by using low-quality machine learning models for human-centric metaverse, e.g., it cannot scrutinize and arrive at decisions accurately and timely. To resolve this pressing issue, we propose MetaFul a federated unlearning solution which reduces the negative influences of low-quality data with no data transmission by removing low-quality training models at the server side. To be specific, MetaFul includes three main components. (i) Low-throughput federated learning (LT-FL) addresses the issue of large model transmission in FL by decreasing the dimension and the number of transmitted model parameters. (ii) Loss-based model quality assessment (LM-QA) utilizes the model loss generated in LT-FL to estimate user data quality. (iii) Non-communicative federated unlearning (NC-FUL) revokes the low-quality data impact on the FL model with careful designed federated unlearning at the server side. Both LM-QA and NC-FUL have no communications with clients. Finally, extensive evaluations are conducted to show MetaFul could improve the model accuracy by at least 2.5% and decrease the user perception time by at least 19.3% in human-centric metaverse compared to benchmarks.