FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving

A new framework called Fed-SE has been introduced to enhance the capabilities of Large Language Model (LLM) agents in privacy-constrained environments. This Federated Self-Evolution approach allows agents to evolve locally while aggregating updates globally, addressing challenges such as heterogeneous tasks and sparse rewards that complicate traditional Federated Learning methods.

A new framework called FedMining has been proposed to enhance underground mine safety through secure and privacy-preserving federated learning (FL). This approach allows for decentralized model training using sensor networks to monitor critical parameters, addressing privacy concerns associated with transmitting raw data to centralized servers.

The paper presents a novel approach to Federated Learning (FL) within the Industrial Internet of Things (IIoT), focusing on a satisfaction-aware incentive scheme that utilizes a deep reinforcement learning-based Stackelberg game. This method aims to optimize the balance between model quality and training latency, addressing a significant challenge in distributed model training while ensuring data privacy.

A recent study on monocular depth estimation highlights the disparity between model accuracy and human-like perception, particularly in applications such as autonomous driving and robotics. Researchers evaluated 69 monocular depth estimators using the KITTI dataset, revealing that high accuracy does not necessarily correlate with human-like behavior in depth perception.

A new study introduces a skewness-guided pruning method for multimodal Swin Transformers, aimed at enhancing federated skin lesion classification on edge devices. This method selectively prunes specific layers based on the statistical skewness of their output distributions, addressing the challenges of deploying large, computationally intensive models in medical imaging.

Astra has been introduced as an interactive general world model capable of generating real-world futures for diverse scenarios, including autonomous driving and robot grasping, utilizing an autoregressive denoising architecture and temporal causal attention to enhance action interactions.

A recent study has introduced a new optimization approach in Federated Learning (FL) that minimizes Layerwise Activation Norm to enhance the generalization of models trained in a federated setup. This method addresses the issue of the global model converging to a 'sharp minimum', which can negatively impact its performance across diverse datasets. By imposing a flatness constraint on the Hessian derived from training loss, the study aims to improve model robustness and adaptability.

The recent introduction of TrajMoE, a scene-adaptive trajectory planning framework, leverages a Mixture of Experts (MoE) architecture combined with Reinforcement Learning to enhance trajectory evaluation in autonomous driving. This approach addresses the variability of trajectory priors across different driving scenarios and improves the scoring mechanism through policy-driven refinement.