AI is graduating from recognition to reasoning—and organizations must follow suit by scaling their computing power with ...

The proposed federated learning system enables model training directly on edge devices, sending only encrypted model updates ...

By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

In an era where cloud-native architectures are at the forefront of digital transformation, regulatory compliance has become ...

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to ...

However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is ...

Moreover, conventional reinforcement learning methods require expensive reward models that may not fully capture the nuanced and subjective nature of human feedback. A team of researchers from China ...

Following our Spring School 2025 of the research semester programme on Control Theory and Reinforcement Learning, we have a general workshop on Themes across Control and Reinforcement Learning. In ...

To overcome these, in this letter we design a decentralized federated learning based network (DFLNet ... attention mechanism and that of inter-cluster is realized by reinforcement learning. The ...

The proposed approach integrates advanced techniques such as autoencoders for anomaly detection, logistic regression for fraud classification, and reinforcement learning ... blockchain integration and ...