With the development of industrial Internet of Things (IIoT), federated learning (FL) is important for protecting sensitive data from various Internet of Things devices (i.e., clients in FL). Despite FL's privacy benefits, attackers (e.g., untrusted clients) can still compromise the performance of the global model through model poisoning attacks. Unfortunately, two key challenges hinder effective detection and impact the performance of the global model in FL: first, accurately identifying malicious models to defend against attacks, and second, efficiently aggregating local models after detecting malicious clients. To address these challenges, we propose an improved FL system based on fuzzy rules, termed RFL-APIA. Compared to the conventional FL system, we have designed two novel components, federated learning generalized depth detection (FedGDD) and Fedsv-Weighted, to enhance performance and mitigate model poisoning attacks. Specifically, FedGDD introduces variance reduction by examining the relationship between local and global model gradients, thereby mitigating interference in nonindependent and identical distributed settings. It further implements an adaptive penalty factor-based scoring system, leveraging variations in local model updates for precise identification and mitigation of attacks. Based on FedGDD's output, the Fedsv-Weighted mechanism dynamically updates the global model's aggregation weights by considering local models' contributions, thus improving model aggregation. Extensive experiments demonstrate that RFL-APIA effectively prevents model poisoning attacks during training, ensuring model security, and guaranteeing a certain level of accuracy and convergence for the global model.

Big data has become a fundamental resource and a commodity in economic activities, thus, it is necessary to build a market model capable of supporting efficient data trading. However, two major challenges remain. First, researches have considered constructing data trading mechanisms, while few of them are based on the method of measuring data value in multiple dimensions. Second, a data market involved an intermediary trading platform (i.e., a third party) which is honest but curious, results may be obtained due the the leakage of private information. In this article, we design TM-OUE, a data trading mechanism based on Optimized Unary Encoding that enables reasonable trading mechanism and protects the privacy of data trading. First of all, we combine qualitative and quantitative methods to measure the value of data in multiple dimensions and formulate data trading model between the data provider and data users. Then, we utilize an Optimized Unary Encoding (OUE) protocol to protect the privacy of the data trading mechanism. Based on the above steps, we develop a two-stage single leader multi-follower Stackelberg game to jointly maximize profits of the data provider and data users. Experimental results demonstrate that TM-OUE can offer appropriate price for data and maximize benefits both for data providers and data users, which guarantees fair data trades while protecting privacy.