Data privacy is becoming increasingly important as data becomes more valuable, as evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a federated learning (FL) system, simply deleting data from the database when a user requests data revocation is not sufficient, as the training data is already implicitly contained in the parameter distribution of the models trained with it. Furthermore, the global model in the FL system is vulnerable to data poisoning attacks by malicious nodes. Exploring a reliable data poisoning reversal method can effectively counter such attacks. In this article, we analyze the necessity of decoupling the processes of unlearning and training and propose a training-agnostic and efficient method that can effectively perform two types of unlearning tasks: 1) client revocation and 2) category removal. Specifically, we decompose the unlearning process into two steps: 1) knowledge erasure and 2) memory guidance. We first propose a novel knowledge erasure strategy called momentum degradation (MoDe) which realizes the erasure of implicit knowledge in the model and ensures that the model can move smoothly to the early state of the retrained model. To mitigate the performance degradation caused by the first step, the memory guidance strategy implements guided fine-tuning of the model on different data points, which can effectively restore the discriminability of the model on the remaining data points. Extensive experiments demonstrate that our method outperforms the existing task-specific algorithms and matches the performance of retraining, accelerating the execution time by 5–20 times compared to retraining on different data sets.

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.

The machine learning performance usually could be improved by training with massive data. However, requesters can only select a subset of devices with limited training data to execute federated learning (FL) tasks as a result of their limited budgets in today’s IoT scenario. To resolve this pressing issue, we devise a blockchain-enhanced FL market (BFL) to$(i)$make data in computationally bounded devices available for training with social Internet of things,$(ii)$maximize the amount of training data with given budgets for an FL task, and$(iii)$decentralize the FL market with blockchain. To achieve these goals, we firstly propose a trust-enhanced collaborative learning strategy (TCL) and a quality-oriented task allocation algorithm (QTA), where TCL enables training data sharing among trusted devices with social Internet of things, and QTA allocates suitable devices to execute FL tasks while maximizing the training quality with fixed budgets. Then, we devise an encrypted model training scheme (EMT) based on a simple but countervailable differential privacy methodology to prevent attacks from malicious devices. In addition, we also propose a contribution-driven delegated proof of stake (DPoS) consensus mechanism to guarantee the fairness of reward distribution in the block generation process. Finally, extensive evaluations are conducted to verify the proposed BFL could improve the total utility of requesters and average accuracy of FL models significantly.