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 ...

This important study presents single-unit activity collected during model-based (MB) and model-free (MF) reinforcement learning in non-human primates. The dataset was carefully collected, and the ...

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 ...

Abstract: Federated learning (FL) is recognized as a pivotal paradigm ... To address this gap, we propose DACPA, a multi-agent deep reinforcement learning (DRL)-based scheme that accounts for client ...

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 ...

One particular focus on large language models has been improving their logical thinking and problem-solving skills. Reinforcement learning (RL) is increasingly used in this space for massive models ...

The design of Flower is based on a few guiding principles: Customizable: Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations ...

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 ...