To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.
Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.
The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs.
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.
We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods.
However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.