AI-based approach

Implementing AI algorithms for edge processing, reducing round-trip times

AI-driven dynamic network slicing for optimised resource allocation based on demand

AI-powered IoT device management for intelligent connectivity and resource allocation

AI for optimising spectrum usage, predictive maintenance, and bandwidth allocation

AI algorithms for dynamic beamforming adjustments based on user locations and network conditions

AI-based anomaly detection, predictive analysis, and real-time threat identification

Machine learning for dynamic resource allocation, optimising performance in real-time

AI-driven energy management, optimising power usage based on traffic patterns and demand

AI-based monitoring and adaptive adjustments for maintaining optimal QoS levels

AI-driven autonomous network management for self-healing, configuration, and optimisation

AI algorithms analysing historical data for predictive maintenance, reducing downtime

AI for efficient task offloading, distributed processing, and edge-to-cloud collaboration

AI-driven algorithms continuously optimising network parameters based on evolving scenarios homomorphic encryption ensure only the combined update is visible, safeguarding individual contributions.

Differential privacy: Imagine adding controlled noise to your emails before contributing them to a spam filter model. This ensures even if the model is compromised, it’s impossible to link any specific email back to you.

Federated transfer learning: Instead of starting from scratch, pre-trained models on public datasets can be used as a base, reducing reliance on sensitive user data. This is like learning a new language by building upon your existing knowledge of a similar one.