AI, Federated Learning Based AI Solutions, AI Ethics

Current AI-based industrial applications have a linear sequential approach for data collection, processing and model deployment cycles where each part of the cycle has a clear task. However, collecting the data required for learning the desired models in one place may not always be possible and centralized data collection may cause data quality issues The recent advances and trends in federated learning address some of these issues in other domains (such as mobile applications). We have a federated learning platform for industrial automation that offers solutions, leveraging systems engineering for AI, by building AI models on decentralized data using a balanced approach to the learning process between centralized and distributed processing to disseminate data allowing accuracy and privacy as well as potential to pay for the data.