Federated AI for Real-Time Smart System Control
Keywords:
Scalable AI Monitoring Systems, Distributed Real-Time Analytics, Federated Artificial Intelligence, Privacy-Preserving Machine Learning, Federated Adaptive Neurons, Large-Scale Sensor Networks, Anomaly Detection in Water Networks, Smart Automation Architectures, High-Performance Distributed Computing, Data Governance and Sovereignty, Real-Time Stream Processing, Smart City Monitoring Systems, Aquatic Environment Surveillance, Confidentiality-Preserving Data Analytics, Horizontal and Vertical Scalability.Abstract
Scalable AI-based systems are outlined that support distributed, real-time monitoring and decision-making in the context of smart automation systems. The architectural principles presented are driven by the following requirements: Distributed sensing and data ingestion; AI methods that support real-time monitoring of large-scale sensor networks; Scalability and performance enhancement; Support for privacy; Governance and data management regulation; Federated capabilities and privacy support preserving data confidentiality. Within this framework, federated adaptive neurons are proposed. These AI nano-scopes provide local information about distributed streams, support anomaly detection and declaration in large sensor networks such as water networks, and can operate in a privacy-preserving environment. Additionally, horizontal and vertical scalability with distributed or high-performance computing is exposed. The proposed principles are supported by experimental cases that illustrate a distributed real-time monitoring capability.
Monitoring of the water quality and surveillance of an aquatic environment is investigated as an application area. A Smart City use case within a European initiative is also proposed. Finally, recent advances regarding governance, security, and privacy of data, especially on personal nature, are examined. These aspects are worthy of special consideration, since data governance regulation is evolving rapidly. The analytics to monitor data that may cross data sovereignty boundaries, such as those originating from users, need to respect end-user privacy preferences.
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