Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices

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 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 has introduced a quantitative framework to evaluate representational similarity metrics, assessing their discriminative capacity across various model families, including CNNs, Vision Transformers, and ConvNeXt. The research utilizes three separability measures to compare commonly used metrics such as RSA and soft matching, revealing that stricter alignment constraints enhance separability.

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 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.

A new framework called VGS-ATD has been introduced to enhance distributed learning for multi-label medical image classification, addressing challenges posed by heterogeneous and imbalanced data. This approach leverages decentralized learning methods like federated and swarm learning, which allow for model training on local nodes while maintaining data privacy by sharing only model weights.

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.

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.