Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety

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 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.

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.

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.

The introduction of FedSCAl addresses the Federated Source-Free Domain Adaptation (FFreeDA) challenge, where clients possess unlabeled data with significant domain gaps. This framework utilizes a Server-Client Alignment mechanism to enhance the reliability of pseudo-labels generated during training, improving the adaptation process in federated learning environments.

The introduction of Federated Deep Supervision and Regularization (FedDSR) aims to enhance the training of autonomous driving models through Federated Learning (FL), addressing challenges such as poor generalization and slow convergence due to non-IID data from diverse driving environments. FedDSR incorporates multi-access intermediate layer supervision and regularization strategies to optimize model performance.