MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System

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 recent study highlights the significance of Machine Unlearning in enhancing privacy within Machine Learning models. By enabling the selective removal of data from trained models, techniques such as Naive Retraining and Exact Unlearning via the SISA framework are evaluated for their computational costs and feasibility using the HSpam14 dataset. The research emphasizes the integration of unlearning principles into Positive Unlabeled Learning to tackle challenges posed by partially labeled datasets.

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 recent study highlights the integration of Machine Learning (ML) and Deep Learning (DL) with Digital Signal Processing (DSP) and Digital Image Processing (DIP), showcasing transformative applications in image enhancement, filtering techniques, and pattern recognition. The research emphasizes the use of frameworks like Discrete Fourier Transform (DFT) and Python for real-time data processing.

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.

China is positioning itself as a leader in global artificial intelligence (AI) governance, aiming to establish comprehensive regulations that could shape international standards and practices in AI. This initiative reflects China's strategic intent to influence the global AI landscape and ensure its technologies align with its national interests.